case study on effects of industrial pollution

• Open access
• Published: 18 June 2021

Is industrial pollution detrimental to public health? Evidence from the world’s most industrialised countries

• Mohammad Mafizur Rahman 1 ,
• Khosrul Alam 2 &
• Eswaran Velayutham 1  

BMC Public Health volume  21 , Article number:  1175 ( 2021 ) Cite this article

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Industrial pollution is considered to be a detrimental factor for human health. This study, therefore, explores the link between health status and industrial pollution for the top 20 industrialised countries of the world.

Crude death rate is used to represent health status and CO 2 emissions from manufacturing industries and construction, and nitrous oxide emissions are considered to be indicators of industrial pollution. Using annual data of 60 years (1960–2019), an unbalanced panel data estimation method is followed where (Driscoll, J. C. et al. Rev Econ Stat, 80, 549–560, 1998) standard error technique is employed to deal with heteroscedasticity, autocorrelation and cross-sectional dependence problems.

The research findings indicate that industrial pollution arising from both variables has a detrimental impact on human health and significantly increases the death rate, while an increase in economic growth, number of physicians, urbanisation, sanitation facilities and schooling decreases the death rate.

Conclusions

Therefore, minimisation of industrial pollution should be the topmost policy agenda in these countries. All the findings are consistent theoretically, and have empirical implications as well. The policy implication of this study is that the mitigation of industrial pollution, considering other pertinent factors, should be addressed appropriately by enunciating effective policies to reduce the human death rate and improve health status in the studied panel countries.

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Introduction

The link between environmental pollution and human health is discussed in a variety of literature, and is still a crucial issue of research, especially in the current COVID-19 pandemic situation. Environmental pollution contributes to climate change, which has various negative impacts on human health like perinatal disorders, infant mortality, respiratory disorders, allergies, malignancies, cardiovascular disorders, increase in oxidative stress, endothelial dysfunction, and mental disorders [ 1 , 2 ]. The impacts on health can be so severe that they lead to death; nearly 7 million people die every year from the interaction of fine particles in polluted air (Goal- 3 of SDGs, [ 3 ]). Global environmental pollution is largely a result of people’s activities through urbanization, industrialization, large-scale petrochemical use, power generation, heavy industry, and mining and exploration, all of which adversely affect the health of local communities through their working and residential actions [ 4 , 5 ]. Therefore, the matter of pollution distress has attracted contemporary global attention due to its acute long-run consequences on human health. Despite this global attention, there are still some policy uncertainties in the existing strategies due to the lack of a wide-ranging and comprehensive study that clearly addresses the adverse impact of industrial pollution on human health status.

Against this backdrop, this study has considered the connection between industrial pollution and health status in the world’s 20 most industrialised countries (see section 3.1). These countries have a total population of 4.57 billion (60% of the world’s population) where the total real GDP is US$66347.02 billion, reflecting 78.18% of the world’s real GDP [ 6 ]. The industrial value addition of these countries is US$17,783.95, which is 75.38% of world’s total industrial value addition [ 6 ]. The average crude death rate in this region is 8.23 per 1000 population, while the total CO 2 emissions are 25,576.7 million tonnes covering 74.85% of the world’s emissions, and the nitrous oxide emissions are 18,37,026.82 thousand metric tons of CO 2 equivalent, which is 58.25% of the world’s emissions [ 6 , 7 ]. Hence our study is comprehensive and explores a vital issue in relation to both the environment and human health.

Some past studies (see [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], among others) have attempted to discover the factors that have impacts on human health and the death rate. However, based on their contradictory findings it has been difficult to draw conclusive and comprehensive guidelines for formulating certain policy initiatives. An inclusive and rigorous probe is necessary to achieve effective policy recommendations. From this perspective, this study is a conscientious venture to reduce the death rate by mitigating industrial pollution, considering the other controlled variables like economic growth, medical attention, drinking water services, sanitation services, secondary school enrolment and urbanisation in a panel of world’s most 20 industrialised countries.

The rationale for considering the desired variables lies in both the theoretical and conceptual notions and previous literary works. The CO 2 and nitrous oxide emissions create industrial pollution which leads to the increase in the human death rate by generating pollution borne diseases ([ 12 , 13 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]; and [ 24 ]). Similarly, having access to the required number of physicians also plays an important role in reducing the mortality rate by providing more effective health care services ([ 11 , 25 , 26 , 27 , 28 ]; and [ 29 ]). The access to clean water facilities, another vital element, decreases the death rate by satisfying basic needs (see [ 14 , 16 , 27 , 30 , 31 ]). Adequate and sufficient sanitation facilities also play a significant role in improving human health and reducing the mortality rate by maintaining safety and hygiene [ 8 , 14 , 15 , 16 , 27 , 30 , 31 , 32 ]. In the same way, education facilities help to increase awareness about health consciousness and this may play a role in lowering the death rate ([ 10 , 33 , 34 , 35 ]; Ray and Linden, 2020 [ 8 , 25 ];). While a greater urbanisation rate increases different health related amenities which reduces mortality rate on the one hand, it also increases mortality by increasing pollution due to different urban activities ([ 9 , 16 , 17 , 32 , 36 , 37 ]; and [ 27 ]). Therefore, more detailed investigation is still needed to assess the role of associated elements on human health.

The major contributions of this study can be noted as: (i) to the best of our knowledge, this is the first study in the literature that investigates the causative elements of the death rate in 20 of the world’s most industrialised countries; (ii) this study has used the most recent and wide-ranging data period of 60 years (1960-2019); (iii) the robust outcomes are achieved by employing an rigorous econometric approach like Driscoll and Kraay’s [ 38 ] standard error technique (details are in section 3.3); (iv) comprehensive policy recommendations, based on the results, are delivered for researchers and policy makers to address the issue of industrial pollution along with other relevant factors for reducing the death rate and improving human health by undertaking effective policy measures.

This research is designed in the following manner: following the introduction, section 2 provides the review of the past literatures; section 3 explains the data and methodology; section 4 displays and analyzes the empirical results; and section 5 provides the conclusion and policy recommendations.

Literature review

Industrial pollution has many adverse consequences on human health and may be a cause of death because of respiratory, lung and cardio-related diseases (see [ 12 , 13 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]; and [ 24 ], among others). Leogrande et al. [ 13 ] discovered the significant impact of industrial air pollution on respiratory mortality in the Taranto area, Southern Italy by applying a difference-in-differences approach from the data of 1998–2014. Further, Karimi and Shokrinezhad [ 12 ] observed that PM 2.5 , PM 10 , CO, NO 2 , and SO 2 were positively and significantly linked with both infant and child under-five mortality for 27 countries for the data period of 1992–2018. Bauleo et al. [ 19 ] found a positive association between industrial PM 10 and mortality from non-accidental causes, all cancers, and cardiac diseases, for the industrial area of Civitavecchia, Italy. Similarly, Næss et al. [ 21 ] identified that the air pollutant indicators (NO 2 , PM 10 , and PM 2.5 ) had a significant effect on all causes of death for both men and women in Oslo, Norway. Similar findings were also identified in the works of Anwar et al. [ 18 ] for 12 of the most vulnerable Asian countries, Shan et al. [ 23 ] for China, Lehtomäki et al. [ 20 ] for Nordic countries, Rajak and Chattopadhyay [ 22 ] for India, and Brito [ 39 ] for Portugal. Clancy et al. [ 40 ] maintained that control of the particulate air pollution significantly reduces the death rate from the comparison of pre and post 72 months ban on coal sales in Dublin, Ireland. In case of CO 2 related pollution, Duong and Jayanthakumaran [ 41 ] found that an increase in CO 2 emissions caused poor health for 60 provinces in Vietnam. Shobande [ 16 ] also demonstrated that the CO 2 emissions increased infant and child mortality in the case of 23 African countries. Dedoussi et al. [ 42 ] and Im et al. [ 43 ] obtained evidence of pre-mature mortality due to cross state air pollution in the USA, and in four Nordic countries, respectively. In the case of the COVID-19 pandemic, Fareed et al. [ 24 ] ascertained that air pollution led to the increase of COVID-19 related mortality in Wuhan, China where daily data from 21 January 2020–31 March 2020 were used. Similar findings of COVID-19 related mortality were also observed by Coker et al. [ 44 ] for northern Italy, Isphording and Pestel [ 45 ] for Germany, Marquès et al. [ 46 ] for Spain, and Gupta et al. [ 47 ] for nine Asian cities. On the other hand, Karuppasamy et al. [ 48 ] found that the improved air quality due to reduced pollution decreased the mortality rate due to COVID-19 for India. However, Cheung et al. [ 49 ] obtained the statistically insignificant impact of the air pollution on cardio-respiratory mortality in Hong Kong. In their studies, the researchers did not focus on the rigorous estimation regarding the impact of the CO 2 emissions and nitrous oxide emissions as outcomes of industrial pollution on the death rate, considering the panel of most industrialised countries.

Some works that considered CO 2 emissions on the relationship with other variables were also found; in the literature (see Mehmood [ 50 ] for Singapore, and Mehmood and Tariq [ 51 , 52 ], and Mehmood et al. [ 53 , 54 ] for South Asian countries; Mehmood et al. [ 50 ] for 3 developing countries, and Mehmood [ 54 ] for South Asian countries). However, these studies did not show the impact of CO 2 emissions, along with other relevant factors, on human health.

Thus, more critical analysis regarding the effect of industrial pollution on human health covering the most industrialised countries deserves further attention.

The death rate in a country is also related to the number of physicians employed in that country (see [ 11 , 25 , 26 , 27 , 28 ]; and [ 29 ], among others). In this context, Jebeli et al. [ 11 ] found that there is a strong reverse correlation between the number of physicians and the crude death rate in the case of 26 OECD countries for the period 2000–2012. Muldoon et al. [ 27 ] found that higher physician density rate worked as the significant reducing factor of infant and child mortality rate in the context of 136 UN member countries, where a mixed effects linear regression model based on 2008 cross-sectional data is employed. In the same way, Farahani et al. [ 25 ] found that the physician density reduces infant mortality both in the short run and long run, where global data of 1960–2000 are used. Using semi-parametric analysis, Liebert and Mäder [ 26 ] also observed that higher physician density reduced infant mortality in Germany from the data of 1928–1936. Russo et al. [ 28 ] found that the supply of primary care physicians contributed to the reduction of infant mortality in Brazil during 2005–2012. Shetty and Shetty [ 29 ] also concluded that the number of doctors per capita had an opposing affiliation with the mortality rate in the case of Asian countries. The role of more physicians in the most industrialised countries to ensure lower mortality rate has not been observed in the past literature. Therefore, inclusion of the number of physicians or its density in death rate analysis is essential to reaffirm its role.

Access to clean water facilities can also influence the mortality rate (see [ 14 , 16 , 27 , 30 , 31 ], among others). Ezeh et al. [ 31 ] found that the risk of mortality from unimproved water was significantly higher in Nigeria, where pooled data of 2003, 2008 and 2013 of Nigeria Demographic and Health Survey were used. Likewise, applying ordinal logistic regression, Cheng et al. [ 30 ] observed that the access to water significantly reduced infant, child, and maternal mortality in the case of 193 countries. Similar results were also found by Muldoon et al. [ 27 ] for 136 UN member countries. Lu et al. [ 14 ] ascertained that improved water facilities reduced the infant mortality rate in the case of 84 developing economies during 1995–2013. In contrast, Shobande [ 16 ] found no significant impact of improved water sources on infant and child mortality in 23 African countries. More research related to the necessity of access to clean water in the panel of industrial countries is urgently needed, but is not found in the existing literature. These findings have emphasised the need for more exploration of the importance of access to clean water facilities on the mortality rate.

The importance of sanitation facilities on the mortality rate cannot be ignored and a number of studies have revealed their role (see [ 8 , 14 , 15 , 16 , 27 , 30 , 31 , 32 ]; among others). Rahman et al. [ 15 ] found that sanitation facilities significantly reduced the crude death rate and infant mortality rate in the case of 15 SAARC-ASEAN countries for the data period of 1995–2014 where fixed effect, random effect and GMM estimator were employed. Ezeh et al. [ 31 ] also found the impact of unimproved sanitation facilities on mortality in Nigeria. Lu et al. [ 14 ] and Alemu [ 8 ] observed that improved sanitation facilities reduced infant mortality rate for 84 developing economies, and for 33 African countries, respectively. Likewise, Cavalcanti et al. [ 32 ] found that the household sanitary facilities reduced child mortality in Brazil. Furthermore, Shobande [ 16 ] discovered that sanitation facilities had a significant impact on infant and child mortality in 23 African countries. In the same way, Muldoon et al. [ 27 ] and Cheng et al. [ 30 ] found that access to sanitation facilities reduced infant, child, and maternal mortality for 136 UN member countries and for 193 countries, respectively. However, in the previous studies, the importance of sanitation facilities on death rate in terms of a panel of most industrialised countries has been ignored. Thus it is important to identify the nexus between sanitation facilities and the mortality rate as a way of implementing improved policy initiatives.

Education reduces the mortality rate by increasing consciousness and responsiveness, as explicated in a number of empirical works, for example Halpern-Manners et al. [ 10 ], Doniec et al. [ 34 ], Buckles et al. [ 33 ], Sajedinejad et al. [ 35 ], Ray and Linden (2020), Alemu [ 8 ], Farahani et al. [ 25 ], among others. Halpern-Manners et al. [ 10 ] observed the robust relationships between education and mortality in the case of the USA. Similarly, Doniec et al. [ 34 ] discerned that those in lower educational groups were significantly more likely to die in the case of 3 Eastern European countries covering the period of 1982–2013. Buckles et al. [ 33 ] also found that college education reduced the mortality in Vietnam. Furthermore, Sajedinejad et al. [ 35 ] identified that education reduced the maternal mortality rate in 179 countries Ray and Linden (2020) observed that the primary education rate significantly reduced infant mortality in 195 countries during the data period of 1995–2014. Similar statistics were also been obtained by Alemu [ 8 ] in the case of 33 African countries from 1994 to 2013. However, Farahani et al. [ 25 ] and Anwar et al. [ 18 ] found no statistically significant impact of education on infant and neonatal mortality for the global panel and the 12 most vulnerable Asian countries, respectively. By critically analysing the research, it has been observed that past studies did not consider the significance of education in determining the death rate in the panel of most industrialised countries. Therefore, because the nexus between education and mortality is not clear, there is a need for investigations into this issue.

Urbanization can also play a significant role in the mortality rate. In this perspective, Bandyopadhyay and Green [ 9 ] found evidence of robust negative correlation between crude death rate and urbanization in the case of the global context. However, Brueckner [ 36 ] found no significant negative correlation between adult mortality and urbanization in sub-Saharan Africa during the data period of 1960–2013. Similarly, Wang [ 37 ] found that the increase in urbanization is significantly linked with reduced mortality, under-five mortality, and infant mortality in 163 countries during 1990–2012, and the effect is stronger in high-income nations. In contrast, Cavalcanti et al. [ 32 ] observed that the urbanization rate significantly increases child mortality in Brazil. Similarly, Taghizadeh-Hesary et al. [ 17 ] found that the urbanization rate increased the risks of lung and respiratory diseases in the case of 18 Asian countries. However, Shobande [ 16 ], and Muldoon et al. [ 27 ] found no significant impact of urbanization on infant and child mortality for 23 African countries, and for 136 UN member countries, respectively. The consideration of urbanization in the context of the panel of highly industrial countries has not been found in previous literature. Moreover, the ambiguous identification of impact of urbanization on mortality rate demands more thorough investigation.

From the current literature it may be observed that the existing identifications regarding the impact of industrial pollution, density of physicians, access to clean water and sanitation facilities, education, and the urbanization rate on human health status are not unanimous and conclusive. Furthermore, the findings of the noted determining factors on the death rate as a group in the world’s most 20 industrialised countries are absent. Therefore, the present study is an endeavour to fill up the existing literature gap to formulate efficacious and durable policy in the health sector.

Data and methodologies

Selection of countries.

This study explores the relationship between health status and industrial pollution in the world’s 20 most industrialised countries. Based on manufacturing, value added (current US$), these industrialised countries Footnote 1 are selected. The countries are China, United States, Japan, Germany, South Korea, India, Italy, France, United Kingdom, Mexico, Indonesia, Russia, Brazil, Canada, Spain, Turkey, Thailand, Switzerland, Ireland and Netherland.

• Unbalanced panel data

This is a panel data study covering the data period 1960–2019. Our data are unbalanced due to the unavailability of all data for the entire period for all sample countries. The data for this study are collected from the World Development Indicator [ 6 ] published by the World Bank. The used data for the selected variables are on crude death rate (per 1000 people), CO2 emissions from manufacturing industries and construction (% of total fuel combustion), nitrous oxide emissions (thousand metric tons of CO2 equivalent), gross domestic product (GDP) per capita (constant 2010 US$), physicians (per 1000 people), urban population (% of total population), people using at least basic drinking water services (% of population), people using at least basic sanitation services (% of population) and secondary school enrolment (% of gross).

Theory, data, econometric approach and estimation software

Grossman [ 55 ] introduces the health production function, which explains the link between health input and an individual’s health output. The individual health production function can be explained as below:

where HO indicates an individual’s health output and HI denotes input needed for an individual’s health. The above model investigates individual health outcome at the micro level. We examine the impact of industrial pollution on health status at the macro level. Following Majeed and Ozturk’s [ 56 ] study, we converted the above model to macro level. The health inputs are divided into economic, environmental and social factors ([ 56 , 57 , 58 ], which can be expressed by the below equation:

Adding healthcare factors, we have extended the eq. ( 2 ). Hence, eq. ( 2 ) can be written as follows:

We used two environmental variables that arose from industrial pollution: CO2 emissions from manufacturing industries and construction, and nitrous oxide emissions. We then developed two models using two industrial pollution variables. The first and second models consisted of CO2 emissions and Nitrous oxide emissions, respectively. In addition to environmental factors, we have also added a range of economic, healthcare and social factors in our models: GDP per capita (economic factor), doctor/population ratio, sanitation, drinking water (healthcare facility factors), secondary school enrolment and urbanisation (social factors). Hence, our models for empirical investigation is as follows:

where DEA is our dependent variable that represents the death rate. The right-hand side variables are explanatory variables where CO2, NIT, GDP, PHY, WAT, SAN, SCH and URB denote CO2 emissions, nitrous oxide emissions, GDP per capita, physicians, drinking water services, sanitation services, secondary school enrolment and urbanisation, respectively.

Following previous studies such as those of Majeed and Ozturk [ 56 ] and Siddique and Kiani [ 58 ] among others, we also took the logarithm of our variables. One of the main advantages of taking the logarithm is that coefficient estimates will provide the elasticities directly. Therefore, the models for our empirical study will be as follows:

Heteroscedasticity, serial correlations and cross-sectional dependences generally exist in panel data because of an increasing availability of data, rapid urbanization and industrialisation, improvement of sanitation and water facilities on a priority basis, better education opportunities, positive economic development, significant amount of industrial pollution, focus on public health issues and economic globalization. All these factors are more common in the case of world’s most industrialised countries. Moreover, if both the cross-section dimension (N) and the time series dimension ( T ) are large, countries’ economic development may be mutually dependent. Therefore, ignoring the heteroscedasticity, the serial correlations and the cross-sectional dependences may provide inefficient statistical inference [ 59 ].

The standard fixed effect model will not be able to produce robust results if a panel data set contains heteroscedasticity, autocorrelation, and cross-sectional dependence. Therefore, this study adopts Hoechle’s [ 60 ] procedure of the STATA xtscc program that produces Driscoll and Kraay’s [ 38 ] standard error technique for linear panel models. These are consistent for heteroskedasticity and also robust to general forms of cross-sectional dependence to examine the impact of industrial pollution on health status for a panel of industrial countries. For applying Driscoll and Kraay’s [ 38 ] standard error technique, this study follows a two-steps procedure. The average values obtained from the product of independent variables and residuals is the first step. These values in a weighted heteroskedasticity autocorrelation (HAC) estimator will be used to generate standard errors, which now have the added feature of being robust against cross-sectional dependence in the second step [ 61 , 62 , 63 ]. There are a number of advantages of having Driscoll and Kraay’s [ 38 ] standard error technique. First, it is one of the best techniques if there is any scope of heteroscedasticity, cross-sectional dependency and serial correlation in the data [ 64 , 65 , 66 ]. Second, this technique is a non-parametric approach which accommodates flexibility, and large time dimension. Third, this technique can apply in both balanced and unbalanced panel data. Finally, this technique can handle missing values [ 60 ].

Following the methodology of Le and Nguyen [ 67 ]; Ikpesu et al. [ 68 ] we have also employed the Panel-corrected standard error (PCSE) technique for robustness checking and validating our outcomes.

A series of econometric procedures have been applied to check the unbalanced panel data. First, the study checks the presence of heteroscedasticity, serial correlation, and cross-sectional dependence in the panel data. Modified Wald statistics for groupwise heteroskedasticity will be used to see the existence of heteroscedasticity in the data set [ 69 ]. The presence of serial correlation will be tested using Wooldridge [ 70 ]. Pesaran [ 71 ] CD statistic is a diagnostic test that checks the existence of cross-sectional dependence. Only Pesaran’s CD test is adequate while using unbalanced panels [ 60 ]. Pesaran’s [ 71 ] CD test is given as below:

Where \( {\hat{p_{ij}}}^2 \) represents the pairwise cross-sectional correlation coefficient of residuals, and T and N represent the time and cross-sectional dimensions of the panel, respectively. In this setting, the null hypothesis has cross-sectional independence with CD ~ N (0, 1).

Empirical results

Descriptive statistics.

Table  1 presents the descriptive statistics of the variables. The average (median) value of crude death rate is 9.05 (8.70). The minimum and maximum of crude death rate are 4.69 and 25.43, respectively. The mean and median values of CO2 emissions are 21.80 and 21.16, respectively. The average (median) value of nitrous oxide emissions is 83.65 (38.09). The average of GDP per capita, physicians, urbanisation, drinking water services, sanitation services and school enrolment are 21,267.66, 1.99, 62.42, 96.43, 89.11 and 82.71, respectively.

Results of heteroscedasticity and autocorrelation

Table  2 presents the results of heteroscedasticity and autocorrelation. The presences of heteroscedasticity, serial correlation and cross-sectional dependence in panel data have serious problems for econometric analysis. Heteroscedasticity exists when the variance of the disturbance differs across samples [ 72 ]. Autocorrelation is the error term correlated with any variable of the model which is not affected by the error term related to other variables in this model [ 73 ]. Table 2 ensures the existence of heteroscedasticity and autocorrelation.

Results of cross-sectional dependence test

Table  3 reports Pesaran’s [ 71 ] cross-sectional dependence test results. The presence of cross-sectional dependence in a panel study suggests the existence of common unobserved shock among the cross-sectional variables over a time period [ 74 ]. The results show that the null hypothesis of cross-sectional dependence is rejected at the 1% statistical significance level for all variables used in this study implying that there is strong evidence of the presence of cross-sectional dependence.

Results of Driscoll-Kraay standard error estimation

This study estimates regression results using Hoechle’s [ 60 ] procedure with Driscoll-Kraay’s [ 38 ] robust standard error to validate the statistical inferences. All variables except GDP per capita and urbanization are found to be significant at the 1% level while using Eq. ( 5.1 ) in Model 1 of Table  4 . The GDP per capita and urbanization are significant at the 5% level. Carbon emissions positively and significantly contribute to industrial pollution which leads to an increase in the crude death rate, suggesting that a 1% increase in CO2 emissions increases crude death rate by 0.10% in the world’s twenty most industrialised countries. This result is comparable with that of Siddique and Kiani [ 58 ] who found that a 1% increases in CO2 emissions increases infant mortality rate by 0.14, 0.09 and 0.26% in middle-income, upper-middle-income and lower-middle-income countries, respectively.

The impacts of a number of factors on crude death rate: economic growth, availability of physicians, urbanization, accessible sanitation and education are negative and statistically significant indicating that 1% increase in these factors decreases crude death rate by 0.07, 0.12, 0.13, 0.45 and 0.10%, respectively. Since the average income of workers in industrial countries is high, they are likely to have better resources and technologies to reduce the death rate arising from industrial pollution. The residents from industrialised countries can have more accessibility to doctors and healthcare facilities that reduce the death rate from industrial pollutions. Urbanization, sanitation and education have positive effects on health status [ 58 ] that mitigate the crude death rate. Our obtained results confirm this proposition empirically.

Model 2 of Table 4 shows the results using Eq. ( 5.2 ). The effect of nitrous oxide emissions on crude death rate is positive and statistically significant at the 1% level implying that a 1% increase in of nitrous oxide emissions increases crude death rate by 0.07% in the sample countries. This result is also comparable with that of Siddique and Kiani [ 58 ] who find that a 1% increase in nitrous oxide emissions increases infant mortality rate by 0.21, 0.24 and 0.19% in middle-income, upper-middle-income and lower-middle-income countries, respectively. Economic growth, access to physicians, urbanization and sanitation have a negative and significant effect on the crude death rate suggesting that 1% increase in these areas decreases the crude death rate by 0.08, 0.13, 0.17 and 0.39%, respectively. Our findings of economic growth and sanitation facilities are in line with those of Rahman et al. [ 15 ] and Kengnal and Holyachi [ 75 ], and the effect of access to physicians is consistent with the findings of Shahid et al. [ 76 ]. However, our result in relation to urbanisation is contradictory to the findings of Li et al. [ 77 ]. Surprisingly, we have obtained a positive association between crude death rate and basic drinking water services. This result is unexpected and contradictory to the findings of Majeed and Ozturk [ 56 ] who investigated the relationship between infant mortality and safe drinking water, using a global panel sample for the period 1990–2016, and found a negative relationship between infant mortality rate and safe drinking water.

Robustness check

Panel-corrected standard error (PCSE) technique effectively deals with heteroscedasticity, serial correlations, and cross-sectional dependence [ 67 , 68 ]. Therefore, we use PCSE estimation as robustness test to compare our results. Table  5 reports the results of PCSE.

Carbon emissions, nitrous oxide emissions and water have significant positive effects on crude death, which are consistent with the results of Table 4 . The economic growth, access to physicians, and sanitation have a negative and significant effect on the crude death rate. Overall, the results from the PCSE estimate show consistent results with the results of Driscoll-Kraay’s [ 38 ] robust standard error estimates.

Conclusions and policy implications

The current study explores the link between health status and industrial pollution in the world’s 20 most industrialised countries, controlling for some other variables. Crude death rate is used to represent health status, and CO 2 emissions from manufacturing industries and construction, and nitrous oxide emissions are considered as markers for industrial pollution. Using annual data for 60 years (1960–2019), an unbalanced panel data estimation method is followed where Driscoll and Kraay’s [ 38 ] standard error technique is employed to deal with heteroscedasticity, autocorrelation and cross-sectional dependence problems. The research findings indicate that industrial pollution arising from both variables significantly increases the death rate, while an increase in economic growth, number of physicians, urbanisation, sanitation facilities and access to schooling decreases the death rate. Therefore, minimisation of industrial pollution should be the topmost policy agenda in these countries. All the findings are consistent theoretically, and have empirical implications as well. These outcomes also comply with the policy guidelines of United Nations Development Program (UNDP) and World Health Organization (WHO) in addressing industrial pollution along with other factors to substantially reduce the death rate by 2030 and improve the public health status (SDGs’ target 3.9 of Goal 3, [ 78 , 79 ]). The policy implication of this study is: the mitigation of industrial pollution, considering other pertinent factors, should be addressed appropriately by enunciating effective policies to reduce the human death rate and improve health status in the studied panel countries. The following particular recommendations will be useful in this respect:

Reducing industrial pollution: The reduction of industrial pollution (CO 2 and nitrous oxide emissions) is essential in relation to environmentally friendly initiatives, which can play a role in reducing pollution related diseases, and eventually diminish the death rate. In this regard, effective pollution disposal facilities should be introduced and technology should be developed to convert industrial wastages into fresh materials and polluted smoke into clean air. A complete and wide-ranging policy package on reducing industrial pollution should be formulated and executed.

Sustainable economic development: Sustainable economic development along with environmental considerations is required to make the earth habitable for future generations. In this perspective, green development, green growth, green technology and a pollution free environment are urgently needed to improve the habitat for humans and thus reduce the mortality rate. Thus efficient and inclusive policy initiatives to ensure sustainable development will be essential for improving the health status of people by reducing the death rate.

Increasing physician numbers: Physicians provide better medical services and guidelines for taking proper drugs and medicines leading to cures from various diseases and improving living standards, which both decrease the mortality rate. In this perspective, a concrete policy venture for generating abundant physician numbers and ease access to them to protect human health is essential.

Enhancing access to clean water and sanitation facilities: Wide access to clean water and better sanitation facilities is required to decrease the mortality rate, as contaminated drinking water and unhygienic sanitation may cause an increase in the death rate. A well-thought out and well-accepted policy initiative is required to facilitate access to clean water and sanitation facilities to all people.

Enlarging educational opportunities: Educational facilities increase awareness of medical and health consciousness to protect people from various fatal diseases and therefore reduce the death rate. An effective policy formulation is necessary to enlarge widespread educational facilities among the entire population to reduce the mortality rate.

Well-organized urbanization: Well-planned urban facilities may increase living standards and increase quality of life by establishing well-organized housing, industries, hospitals, supplying electricity, clean water and hygienic sanitation facilities. Therefore, a complete well-organized urbanization policy design is urgently needed in conjunction with other policies.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Data source for these countries: World Bank national accounts data, and OECD National Accounts data files

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Rahman, M.M., Alam, K. & Velayutham, E. Is industrial pollution detrimental to public health? Evidence from the world’s most industrialised countries. BMC Public Health 21 , 1175 (2021). https://doi.org/10.1186/s12889-021-11217-6

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Environmental and Health Impacts of Air Pollution: A Review

Ioannis manisalidis, elisavet stavropoulou, agathangelos stavropoulos, eugenia bezirtzoglou.

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Edited by: Ethel Eljarrat, Institute of Environmental Assessment and Water Research (CSIC), Spain

Reviewed by: Fei Li, Zhongnan University of Economics and Law, China; M. Jahangir Alam, University of Houston, United States

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Elisavet Stavropoulou [email protected]

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Received 2019 Oct 17; Accepted 2020 Jan 17; Collection date 2020.

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One of our era's greatest scourges is air pollution, on account not only of its impact on climate change but also its impact on public and individual health due to increasing morbidity and mortality. There are many pollutants that are major factors in disease in humans. Among them, Particulate Matter (PM), particles of variable but very small diameter, penetrate the respiratory system via inhalation, causing respiratory and cardiovascular diseases, reproductive and central nervous system dysfunctions, and cancer. Despite the fact that ozone in the stratosphere plays a protective role against ultraviolet irradiation, it is harmful when in high concentration at ground level, also affecting the respiratory and cardiovascular system. Furthermore, nitrogen oxide, sulfur dioxide, Volatile Organic Compounds (VOCs), dioxins, and polycyclic aromatic hydrocarbons (PAHs) are all considered air pollutants that are harmful to humans. Carbon monoxide can even provoke direct poisoning when breathed in at high levels. Heavy metals such as lead, when absorbed into the human body, can lead to direct poisoning or chronic intoxication, depending on exposure. Diseases occurring from the aforementioned substances include principally respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD), asthma, bronchiolitis, and also lung cancer, cardiovascular events, central nervous system dysfunctions, and cutaneous diseases. Last but not least, climate change resulting from environmental pollution affects the geographical distribution of many infectious diseases, as do natural disasters. The only way to tackle this problem is through public awareness coupled with a multidisciplinary approach by scientific experts; national and international organizations must address the emergence of this threat and propose sustainable solutions.

Keywords: air pollution, environment, health, public health, gas emission, policy

Approach to the Problem

The interactions between humans and their physical surroundings have been extensively studied, as multiple human activities influence the environment. The environment is a coupling of the biotic (living organisms and microorganisms) and the abiotic (hydrosphere, lithosphere, and atmosphere).

Pollution is defined as the introduction into the environment of substances harmful to humans and other living organisms. Pollutants are harmful solids, liquids, or gases produced in higher than usual concentrations that reduce the quality of our environment.

Human activities have an adverse effect on the environment by polluting the water we drink, the air we breathe, and the soil in which plants grow. Although the industrial revolution was a great success in terms of technology, society, and the provision of multiple services, it also introduced the production of huge quantities of pollutants emitted into the air that are harmful to human health. Without any doubt, the global environmental pollution is considered an international public health issue with multiple facets. Social, economic, and legislative concerns and lifestyle habits are related to this major problem. Clearly, urbanization and industrialization are reaching unprecedented and upsetting proportions worldwide in our era. Anthropogenic air pollution is one of the biggest public health hazards worldwide, given that it accounts for about 9 million deaths per year ( 1 ).

Without a doubt, all of the aforementioned are closely associated with climate change, and in the event of danger, the consequences can be severe for mankind ( 2 ). Climate changes and the effects of global planetary warming seriously affect multiple ecosystems, causing problems such as food safety issues, ice and iceberg melting, animal extinction, and damage to plants ( 3 , 4 ).

Air pollution has various health effects. The health of susceptible and sensitive individuals can be impacted even on low air pollution days. Short-term exposure to air pollutants is closely related to COPD (Chronic Obstructive Pulmonary Disease), cough, shortness of breath, wheezing, asthma, respiratory disease, and high rates of hospitalization (a measurement of morbidity).

The long-term effects associated with air pollution are chronic asthma, pulmonary insufficiency, cardiovascular diseases, and cardiovascular mortality. According to a Swedish cohort study, diabetes seems to be induced after long-term air pollution exposure ( 5 ). Moreover, air pollution seems to have various malign health effects in early human life, such as respiratory, cardiovascular, mental, and perinatal disorders ( 3 ), leading to infant mortality or chronic disease in adult age ( 6 ).

National reports have mentioned the increased risk of morbidity and mortality ( 1 ). These studies were conducted in many places around the world and show a correlation between daily ranges of particulate matter (PM) concentration and daily mortality. Climate shifts and global planetary warming ( 3 ) could aggravate the situation. Besides, increased hospitalization (an index of morbidity) has been registered among the elderly and susceptible individuals for specific reasons. Fine and ultrafine particulate matter seems to be associated with more serious illnesses ( 6 ), as it can invade the deepest parts of the airways and more easily reach the bloodstream.

Air pollution mainly affects those living in large urban areas, where road emissions contribute the most to the degradation of air quality. There is also a danger of industrial accidents, where the spread of a toxic fog can be fatal to the populations of the surrounding areas. The dispersion of pollutants is determined by many parameters, most notably atmospheric stability and wind ( 6 ).

In developing countries ( 7 ), the problem is more serious due to overpopulation and uncontrolled urbanization along with the development of industrialization. This leads to poor air quality, especially in countries with social disparities and a lack of information on sustainable management of the environment. The use of fuels such as wood fuel or solid fuel for domestic needs due to low incomes exposes people to bad-quality, polluted air at home. It is of note that three billion people around the world are using the above sources of energy for their daily heating and cooking needs ( 8 ). In developing countries, the women of the household seem to carry the highest risk for disease development due to their longer duration exposure to the indoor air pollution ( 8 , 9 ). Due to its fast industrial development and overpopulation, China is one of the Asian countries confronting serious air pollution problems ( 10 , 11 ). The lung cancer mortality observed in China is associated with fine particles ( 12 ). As stated already, long-term exposure is associated with deleterious effects on the cardiovascular system ( 3 , 5 ). However, it is interesting to note that cardiovascular diseases have mostly been observed in developed and high-income countries rather than in the developing low-income countries exposed highly to air pollution ( 13 ). Extreme air pollution is recorded in India, where the air quality reaches hazardous levels. New Delhi is one of the more polluted cities in India. Flights in and out of New Delhi International Airport are often canceled due to the reduced visibility associated with air pollution. Pollution is occurring both in urban and rural areas in India due to the fast industrialization, urbanization, and rise in use of motorcycle transportation. Nevertheless, biomass combustion associated with heating and cooking needs and practices is a major source of household air pollution in India and in Nepal ( 14 , 15 ). There is spatial heterogeneity in India, as areas with diverse climatological conditions and population and education levels generate different indoor air qualities, with higher PM 2.5 observed in North Indian states (557–601 μg/m 3 ) compared to the Southern States (183–214 μg/m 3 ) ( 16 , 17 ). The cold climate of the North Indian areas may be the main reason for this, as longer periods at home and more heating are necessary compared to in the tropical climate of Southern India. Household air pollution in India is associated with major health effects, especially in women and young children, who stay indoors for longer periods. Chronic obstructive respiratory disease (CORD) and lung cancer are mostly observed in women, while acute lower respiratory disease is seen in young children under 5 years of age ( 18 ).

Accumulation of air pollution, especially sulfur dioxide and smoke, reaching 1,500 mg/m3, resulted in an increase in the number of deaths (4,000 deaths) in December 1952 in London and in 1963 in New York City (400 deaths) ( 19 ). An association of pollution with mortality was reported on the basis of monitoring of outdoor pollution in six US metropolitan cities ( 20 ). In every case, it seems that mortality was closely related to the levels of fine, inhalable, and sulfate particles more than with the levels of total particulate pollution, aerosol acidity, sulfur dioxide, or nitrogen dioxide ( 20 ).

Furthermore, extremely high levels of pollution are reported in Mexico City and Rio de Janeiro, followed by Milan, Ankara, Melbourne, Tokyo, and Moscow ( 19 ).

Based on the magnitude of the public health impact, it is certain that different kinds of interventions should be taken into account. Success and effectiveness in controlling air pollution, specifically at the local level, have been reported. Adequate technological means are applied considering the source and the nature of the emission as well as its impact on health and the environment. The importance of point sources and non-point sources of air pollution control is reported by Schwela and Köth-Jahr ( 21 ). Without a doubt, a detailed emission inventory must record all sources in a given area. Beyond considering the above sources and their nature, topography and meteorology should also be considered, as stated previously. Assessment of the control policies and methods is often extrapolated from the local to the regional and then to the global scale. Air pollution may be dispersed and transported from one region to another area located far away. Air pollution management means the reduction to acceptable levels or possible elimination of air pollutants whose presence in the air affects our health or the environmental ecosystem. Private and governmental entities and authorities implement actions to ensure the air quality ( 22 ). Air quality standards and guidelines were adopted for the different pollutants by the WHO and EPA as a tool for the management of air quality ( 1 , 23 ). These standards have to be compared to the emissions inventory standards by causal analysis and dispersion modeling in order to reveal the problematic areas ( 24 ). Inventories are generally based on a combination of direct measurements and emissions modeling ( 24 ).

As an example, we state here the control measures at the source through the use of catalytic converters in cars. These are devices that turn the pollutants and toxic gases produced from combustion engines into less-toxic pollutants by catalysis through redox reactions ( 25 ). In Greece, the use of private cars was restricted by tracking their license plates in order to reduce traffic congestion during rush hour ( 25 ).

Concerning industrial emissions, collectors and closed systems can keep the air pollution to the minimal standards imposed by legislation ( 26 ).

Current strategies to improve air quality require an estimation of the economic value of the benefits gained from proposed programs. These proposed programs by public authorities, and directives are issued with guidelines to be respected.

In Europe, air quality limit values AQLVs (Air Quality Limit Values) are issued for setting off planning claims ( 27 ). In the USA, the NAAQS (National Ambient Air Quality Standards) establish the national air quality limit values ( 27 ). While both standards and directives are based on different mechanisms, significant success has been achieved in the reduction of overall emissions and associated health and environmental effects ( 27 ). The European Directive identifies geographical areas of risk exposure as monitoring/assessment zones to record the emission sources and levels of air pollution ( 27 ), whereas the USA establishes global geographical air quality criteria according to the severity of their air quality problem and records all sources of the pollutants and their precursors ( 27 ).

In this vein, funds have been financing, directly or indirectly, projects related to air quality along with the technical infrastructure to maintain good air quality. These plans focus on an inventory of databases from air quality environmental planning awareness campaigns. Moreover, pollution measures of air emissions may be taken for vehicles, machines, and industries in urban areas.

Technological innovation can only be successful if it is able to meet the needs of society. In this sense, technology must reflect the decision-making practices and procedures of those involved in risk assessment and evaluation and act as a facilitator in providing information and assessments to enable decision makers to make the best decisions possible. Summarizing the aforementioned in order to design an effective air quality control strategy, several aspects must be considered: environmental factors and ambient air quality conditions, engineering factors and air pollutant characteristics, and finally, economic operating costs for technological improvement and administrative and legal costs. Considering the economic factor, competitiveness through neoliberal concepts is offering a solution to environmental problems ( 22 ).

The development of environmental governance, along with technological progress, has initiated the deployment of a dialogue. Environmental politics has created objections and points of opposition between different political parties, scientists, media, and governmental and non-governmental organizations ( 22 ). Radical environmental activism actions and movements have been created ( 22 ). The rise of the new information and communication technologies (ICTs) are many times examined as to whether and in which way they have influenced means of communication and social movements such as activism ( 28 ). Since the 1990s, the term “digital activism” has been used increasingly and in many different disciplines ( 29 ). Nowadays, multiple digital technologies can be used to produce a digital activism outcome on environmental issues. More specifically, devices with online capabilities such as computers or mobile phones are being used as a way to pursue change in political and social affairs ( 30 ).

In the present paper, we focus on the sources of environmental pollution in relation to public health and propose some solutions and interventions that may be of interest to environmental legislators and decision makers.

Sources of Exposure

It is known that the majority of environmental pollutants are emitted through large-scale human activities such as the use of industrial machinery, power-producing stations, combustion engines, and cars. Because these activities are performed at such a large scale, they are by far the major contributors to air pollution, with cars estimated to be responsible for approximately 80% of today's pollution ( 31 ). Some other human activities are also influencing our environment to a lesser extent, such as field cultivation techniques, gas stations, fuel tanks heaters, and cleaning procedures ( 32 ), as well as several natural sources, such as volcanic and soil eruptions and forest fires.

The classification of air pollutants is based mainly on the sources producing pollution. Therefore, it is worth mentioning the four main sources, following the classification system: Major sources, Area sources, Mobile sources, and Natural sources.

Major sources include the emission of pollutants from power stations, refineries, and petrochemicals, the chemical and fertilizer industries, metallurgical and other industrial plants, and, finally, municipal incineration.

Indoor area sources include domestic cleaning activities, dry cleaners, printing shops, and petrol stations.

Mobile sources include automobiles, cars, railways, airways, and other types of vehicles.

Finally, natural sources include, as stated previously, physical disasters ( 33 ) such as forest fire, volcanic erosion, dust storms, and agricultural burning.

However, many classification systems have been proposed. Another type of classification is a grouping according to the recipient of the pollution, as follows:

Air pollution is determined as the presence of pollutants in the air in large quantities for long periods. Air pollutants are dispersed particles, hydrocarbons, CO, CO 2 , NO, NO 2 , SO 3 , etc.

Water pollution is organic and inorganic charge and biological charge ( 10 ) at high levels that affect the water quality ( 34 , 35 ).

Soil pollution occurs through the release of chemicals or the disposal of wastes, such as heavy metals, hydrocarbons, and pesticides.

Air pollution can influence the quality of soil and water bodies by polluting precipitation, falling into water and soil environments ( 34 , 36 ). Notably, the chemistry of the soil can be amended due to acid precipitation by affecting plants, cultures, and water quality ( 37 ). Moreover, movement of heavy metals is favored by soil acidity, and metals are so then moving into the watery environment. It is known that heavy metals such as aluminum are noxious to wildlife and fishes. Soil quality seems to be of importance, as soils with low calcium carbonate levels are at increased jeopardy from acid rain. Over and above rain, snow and particulate matter drip into watery ' bodies ( 36 , 38 ).

Lastly, pollution is classified following type of origin:

Radioactive and nuclear pollution , releasing radioactive and nuclear pollutants into water, air, and soil during nuclear explosions and accidents, from nuclear weapons, and through handling or disposal of radioactive sewage.

Radioactive materials can contaminate surface water bodies and, being noxious to the environment, plants, animals, and humans. It is known that several radioactive substances such as radium and uranium concentrate in the bones and can cause cancers ( 38 , 39 ).

Noise pollution is produced by machines, vehicles, traffic noises, and musical installations that are harmful to our hearing.

The World Health Organization introduced the term DALYs. The DALYs for a disease or health condition is defined as the sum of the Years of Life Lost (YLL) due to premature mortality in the population and the Years Lost due to Disability (YLD) for people living with the health condition or its consequences ( 39 ). In Europe, air pollution is the main cause of disability-adjusted life years lost (DALYs), followed by noise pollution. The potential relationships of noise and air pollution with health have been studied ( 40 ). The study found that DALYs related to noise were more important than those related to air pollution, as the effects of environmental noise on cardiovascular disease were independent of air pollution ( 40 ). Environmental noise should be counted as an independent public health risk ( 40 ).

Environmental pollution occurs when changes in the physical, chemical, or biological constituents of the environment (air masses, temperature, climate, etc.) are produced.

Pollutants harm our environment either by increasing levels above normal or by introducing harmful toxic substances. Primary pollutants are directly produced from the above sources, and secondary pollutants are emitted as by-products of the primary ones. Pollutants can be biodegradable or non-biodegradable and of natural origin or anthropogenic, as stated previously. Moreover, their origin can be a unique source (point-source) or dispersed sources.

Pollutants have differences in physical and chemical properties, explaining the discrepancy in their capacity for producing toxic effects. As an example, we state here that aerosol compounds ( 41 – 43 ) have a greater toxicity than gaseous compounds due to their tiny size (solid or liquid) in the atmosphere; they have a greater penetration capacity. Gaseous compounds are eliminated more easily by our respiratory system ( 41 ). These particles are able to damage lungs and can even enter the bloodstream ( 41 ), leading to the premature deaths of millions of people yearly. Moreover, the aerosol acidity ([H+]) seems to considerably enhance the production of secondary organic aerosols (SOA), but this last aspect is not supported by other scientific teams ( 38 ).

Climate and Pollution

Air pollution and climate change are closely related. Climate is the other side of the same coin that reduces the quality of our Earth ( 44 ). Pollutants such as black carbon, methane, tropospheric ozone, and aerosols affect the amount of incoming sunlight. As a result, the temperature of the Earth is increasing, resulting in the melting of ice, icebergs, and glaciers.

In this vein, climatic changes will affect the incidence and prevalence of both residual and imported infections in Europe. Climate and weather affect the duration, timing, and intensity of outbreaks strongly and change the map of infectious diseases in the globe ( 45 ). Mosquito-transmitted parasitic or viral diseases are extremely climate-sensitive, as warming firstly shortens the pathogen incubation period and secondly shifts the geographic map of the vector. Similarly, water-warming following climate changes leads to a high incidence of waterborne infections. Recently, in Europe, eradicated diseases seem to be emerging due to the migration of population, for example, cholera, poliomyelitis, tick-borne encephalitis, and malaria ( 46 ).

The spread of epidemics is associated with natural climate disasters and storms, which seem to occur more frequently nowadays ( 47 ). Malnutrition and disequilibration of the immune system are also associated with the emerging infections affecting public health ( 48 ).

The Chikungunya virus “took the airplane” from the Indian Ocean to Europe, as outbreaks of the disease were registered in Italy ( 49 ) as well as autochthonous cases in France ( 50 ).

An increase in cryptosporidiosis in the United Kingdom and in the Czech Republic seems to have occurred following flooding ( 36 , 51 ).

As stated previously, aerosols compounds are tiny in size and considerably affect the climate. They are able to dissipate sunlight (the albedo phenomenon) by dispersing a quarter of the sun's rays back to space and have cooled the global temperature over the last 30 years ( 52 ).

Air Pollutants

The World Health Organization (WHO) reports on six major air pollutants, namely particle pollution, ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. Air pollution can have a disastrous effect on all components of the environment, including groundwater, soil, and air. Additionally, it poses a serious threat to living organisms. In this vein, our interest is mainly to focus on these pollutants, as they are related to more extensive and severe problems in human health and environmental impact. Acid rain, global warming, the greenhouse effect, and climate changes have an important ecological impact on air pollution ( 53 ).

Particulate Matter (PM) and Health

Studies have shown a relationship between particulate matter (PM) and adverse health effects, focusing on either short-term (acute) or long-term (chronic) PM exposure.

Particulate matter (PM) is usually formed in the atmosphere as a result of chemical reactions between the different pollutants. The penetration of particles is closely dependent on their size ( 53 ). Particulate Matter (PM) was defined as a term for particles by the United States Environmental Protection Agency ( 54 ). Particulate matter (PM) pollution includes particles with diameters of 10 micrometers (μm) or smaller, called PM 10 , and extremely fine particles with diameters that are generally 2.5 micrometers (μm) and smaller.

Particulate matter contains tiny liquid or solid droplets that can be inhaled and cause serious health effects ( 55 ). Particles <10 μm in diameter (PM 10 ) after inhalation can invade the lungs and even reach the bloodstream. Fine particles, PM 2.5 , pose a greater risk to health ( 6 , 56 ) ( Table 1 ).

Penetrability according to particle size.

Multiple epidemiological studies have been performed on the health effects of PM. A positive relation was shown between both short-term and long-term exposures of PM 2.5 and acute nasopharyngitis ( 56 ). In addition, long-term exposure to PM for years was found to be related to cardiovascular diseases and infant mortality.

Those studies depend on PM 2.5 monitors and are restricted in terms of study area or city area due to a lack of spatially resolved daily PM 2.5 concentration data and, in this way, are not representative of the entire population. Following a recent epidemiological study by the Department of Environmental Health at Harvard School of Public Health (Boston, MA) ( 57 ), it was reported that, as PM 2.5 concentrations vary spatially, an exposure error (Berkson error) seems to be produced, and the relative magnitudes of the short- and long-term effects are not yet completely elucidated. The team developed a PM 2.5 exposure model based on remote sensing data for assessing short- and long-term human exposures ( 57 ). This model permits spatial resolution in short-term effects plus the assessment of long-term effects in the whole population.

Moreover, respiratory diseases and affection of the immune system are registered as long-term chronic effects ( 58 ). It is worth noting that people with asthma, pneumonia, diabetes, and respiratory and cardiovascular diseases are especially susceptible and vulnerable to the effects of PM. PM 2.5 , followed by PM 10 , are strongly associated with diverse respiratory system diseases ( 59 ), as their size permits them to pierce interior spaces ( 60 ). The particles produce toxic effects according to their chemical and physical properties. The components of PM 10 and PM 2.5 can be organic (polycyclic aromatic hydrocarbons, dioxins, benzene, 1-3 butadiene) or inorganic (carbon, chlorides, nitrates, sulfates, metals) in nature ( 55 ).

Particulate Matter (PM) is divided into four main categories according to type and size ( 61 ) ( Table 2 ).

Types and sizes of particulate Matter (PM).

Gas contaminants include PM in aerial masses.

Particulate contaminants include contaminants such as smog, soot, tobacco smoke, oil smoke, fly ash, and cement dust.

Biological Contaminants are microorganisms (bacteria, viruses, fungi, mold, and bacterial spores), cat allergens, house dust and allergens, and pollen.

Types of Dust include suspended atmospheric dust, settling dust, and heavy dust.

Finally, another fact is that the half-lives of PM 10 and PM 2.5 particles in the atmosphere is extended due to their tiny dimensions; this permits their long-lasting suspension in the atmosphere and even their transfer and spread to distant destinations where people and the environment may be exposed to the same magnitude of pollution ( 53 ). They are able to change the nutrient balance in watery ecosystems, damage forests and crops, and acidify water bodies.

As stated, PM 2.5 , due to their tiny size, are causing more serious health effects. These aforementioned fine particles are the main cause of the “haze” formation in different metropolitan areas ( 12 , 13 , 61 ).

Ozone Impact in the Atmosphere

Ozone (O 3 ) is a gas formed from oxygen under high voltage electric discharge ( 62 ). It is a strong oxidant, 52% stronger than chlorine. It arises in the stratosphere, but it could also arise following chain reactions of photochemical smog in the troposphere ( 63 ).

Ozone can travel to distant areas from its initial source, moving with air masses ( 64 ). It is surprising that ozone levels over cities are low in contrast to the increased amounts occuring in urban areas, which could become harmful for cultures, forests, and vegetation ( 65 ) as it is reducing carbon assimilation ( 66 ). Ozone reduces growth and yield ( 47 , 48 ) and affects the plant microflora due to its antimicrobial capacity ( 67 , 68 ). In this regard, ozone acts upon other natural ecosystems, with microflora ( 69 , 70 ) and animal species changing their species composition ( 71 ). Ozone increases DNA damage in epidermal keratinocytes and leads to impaired cellular function ( 72 ).

Ground-level ozone (GLO) is generated through a chemical reaction between oxides of nitrogen and VOCs emitted from natural sources and/or following anthropogenic activities.

Ozone uptake usually occurs by inhalation. Ozone affects the upper layers of the skin and the tear ducts ( 73 ). A study of short-term exposure of mice to high levels of ozone showed malondialdehyde formation in the upper skin (epidermis) but also depletion in vitamins C and E. It is likely that ozone levels are not interfering with the skin barrier function and integrity to predispose to skin disease ( 74 ).

Due to the low water-solubility of ozone, inhaled ozone has the capacity to penetrate deeply into the lungs ( 75 ).

Toxic effects induced by ozone are registered in urban areas all over the world, causing biochemical, morphologic, functional, and immunological disorders ( 76 ).

The European project (APHEA2) focuses on the acute effects of ambient ozone concentrations on mortality ( 77 ). Daily ozone concentrations compared to the daily number of deaths were reported from different European cities for a 3-year period. During the warm period of the year, an observed increase in ozone concentration was associated with an increase in the daily number of deaths (0.33%), in the number of respiratory deaths (1.13%), and in the number of cardiovascular deaths (0.45%). No effect was observed during wintertime.

Carbon Monoxide (CO)

Carbon monoxide is produced by fossil fuel when combustion is incomplete. The symptoms of poisoning due to inhaling carbon monoxide include headache, dizziness, weakness, nausea, vomiting, and, finally, loss of consciousness.

The affinity of carbon monoxide to hemoglobin is much greater than that of oxygen. In this vein, serious poisoning may occur in people exposed to high levels of carbon monoxide for a long period of time. Due to the loss of oxygen as a result of the competitive binding of carbon monoxide, hypoxia, ischemia, and cardiovascular disease are observed.

Carbon monoxide affects the greenhouses gases that are tightly connected to global warming and climate. This should lead to an increase in soil and water temperatures, and extreme weather conditions or storms may occur ( 68 ).

However, in laboratory and field experiments, it has been seen to produce increased plant growth ( 78 ).

Nitrogen Oxide (NO 2 )

Nitrogen oxide is a traffic-related pollutant, as it is emitted from automobile motor engines ( 79 , 80 ). It is an irritant of the respiratory system as it penetrates deep in the lung, inducing respiratory diseases, coughing, wheezing, dyspnea, bronchospasm, and even pulmonary edema when inhaled at high levels. It seems that concentrations over 0.2 ppm produce these adverse effects in humans, while concentrations higher than 2.0 ppm affect T-lymphocytes, particularly the CD8+ cells and NK cells that produce our immune response ( 81 ).It is reported that long-term exposure to high levels of nitrogen dioxide can be responsible for chronic lung disease. Long-term exposure to NO 2 can impair the sense of smell ( 81 ).

However, systems other than respiratory ones can be involved, as symptoms such as eye, throat, and nose irritation have been registered ( 81 ).

High levels of nitrogen dioxide are deleterious to crops and vegetation, as they have been observed to reduce crop yield and plant growth efficiency. Moreover, NO 2 can reduce visibility and discolor fabrics ( 81 ).

Sulfur Dioxide (SO 2 )

Sulfur dioxide is a harmful gas that is emitted mainly from fossil fuel consumption or industrial activities. The annual standard for SO 2 is 0.03 ppm ( 82 ). It affects human, animal, and plant life. Susceptible people as those with lung disease, old people, and children, who present a higher risk of damage. The major health problems associated with sulfur dioxide emissions in industrialized areas are respiratory irritation, bronchitis, mucus production, and bronchospasm, as it is a sensory irritant and penetrates deep into the lung converted into bisulfite and interacting with sensory receptors, causing bronchoconstriction. Moreover, skin redness, damage to the eyes (lacrimation and corneal opacity) and mucous membranes, and worsening of pre-existing cardiovascular disease have been observed ( 81 ).

Environmental adverse effects, such as acidification of soil and acid rain, seem to be associated with sulfur dioxide emissions ( 83 ).

Lead is a heavy metal used in different industrial plants and emitted from some petrol motor engines, batteries, radiators, waste incinerators, and waste waters ( 84 ).

Moreover, major sources of lead pollution in the air are metals, ore, and piston-engine aircraft. Lead poisoning is a threat to public health due to its deleterious effects upon humans, animals, and the environment, especially in the developing countries.

Exposure to lead can occur through inhalation, ingestion, and dermal absorption. Trans- placental transport of lead was also reported, as lead passes through the placenta unencumbered ( 85 ). The younger the fetus is, the more harmful the toxic effects. Lead toxicity affects the fetal nervous system; edema or swelling of the brain is observed ( 86 ). Lead, when inhaled, accumulates in the blood, soft tissue, liver, lung, bones, and cardiovascular, nervous, and reproductive systems. Moreover, loss of concentration and memory, as well as muscle and joint pain, were observed in adults ( 85 , 86 ).

Children and newborns ( 87 ) are extremely susceptible even to minimal doses of lead, as it is a neurotoxicant and causes learning disabilities, impairment of memory, hyperactivity, and even mental retardation.

Elevated amounts of lead in the environment are harmful to plants and crop growth. Neurological effects are observed in vertebrates and animals in association with high lead levels ( 88 ).

Polycyclic Aromatic Hydrocarbons(PAHs)

The distribution of PAHs is ubiquitous in the environment, as the atmosphere is the most important means of their dispersal. They are found in coal and in tar sediments. Moreover, they are generated through incomplete combustion of organic matter as in the cases of forest fires, incineration, and engines ( 89 ). PAH compounds, such as benzopyrene, acenaphthylene, anthracene, and fluoranthene are recognized as toxic, mutagenic, and carcinogenic substances. They are an important risk factor for lung cancer ( 89 ).

Volatile Organic Compounds(VOCs)

Volatile organic compounds (VOCs), such as toluene, benzene, ethylbenzene, and xylene ( 90 ), have been found to be associated with cancer in humans ( 91 ). The use of new products and materials has actually resulted in increased concentrations of VOCs. VOCs pollute indoor air ( 90 ) and may have adverse effects on human health ( 91 ). Short-term and long-term adverse effects on human health are observed. VOCs are responsible for indoor air smells. Short-term exposure is found to cause irritation of eyes, nose, throat, and mucosal membranes, while those of long duration exposure include toxic reactions ( 92 ). Predictable assessment of the toxic effects of complex VOC mixtures is difficult to estimate, as these pollutants can have synergic, antagonistic, or indifferent effects ( 91 , 93 ).

Dioxins originate from industrial processes but also come from natural processes, such as forest fires and volcanic eruptions. They accumulate in foods such as meat and dairy products, fish and shellfish, and especially in the fatty tissue of animals ( 94 ).

Short-period exhibition to high dioxin concentrations may result in dark spots and lesions on the skin ( 94 ). Long-term exposure to dioxins can cause developmental problems, impairment of the immune, endocrine and nervous systems, reproductive infertility, and cancer ( 94 ).

Without any doubt, fossil fuel consumption is responsible for a sizeable part of air contamination. This contamination may be anthropogenic, as in agricultural and industrial processes or transportation, while contamination from natural sources is also possible. Interestingly, it is of note that the air quality standards established through the European Air Quality Directive are somewhat looser than the WHO guidelines, which are stricter ( 95 ).

Effect of Air Pollution on Health

The most common air pollutants are ground-level ozone and Particulates Matter (PM). Air pollution is distinguished into two main types:

Outdoor pollution is the ambient air pollution.

Indoor pollution is the pollution generated by household combustion of fuels.

People exposed to high concentrations of air pollutants experience disease symptoms and states of greater and lesser seriousness. These effects are grouped into short- and long-term effects affecting health.

Susceptible populations that need to be aware of health protection measures include old people, children, and people with diabetes and predisposing heart or lung disease, especially asthma.

As extensively stated previously, according to a recent epidemiological study from Harvard School of Public Health, the relative magnitudes of the short- and long-term effects have not been completely clarified ( 57 ) due to the different epidemiological methodologies and to the exposure errors. New models are proposed for assessing short- and long-term human exposure data more successfully ( 57 ). Thus, in the present section, we report the more common short- and long-term health effects but also general concerns for both types of effects, as these effects are often dependent on environmental conditions, dose, and individual susceptibility.

Short-term effects are temporary and range from simple discomfort, such as irritation of the eyes, nose, skin, throat, wheezing, coughing and chest tightness, and breathing difficulties, to more serious states, such as asthma, pneumonia, bronchitis, and lung and heart problems. Short-term exposure to air pollution can also cause headaches, nausea, and dizziness.

These problems can be aggravated by extended long-term exposure to the pollutants, which is harmful to the neurological, reproductive, and respiratory systems and causes cancer and even, rarely, deaths.

The long-term effects are chronic, lasting for years or the whole life and can even lead to death. Furthermore, the toxicity of several air pollutants may also induce a variety of cancers in the long term ( 96 ).

As stated already, respiratory disorders are closely associated with the inhalation of air pollutants. These pollutants will invade through the airways and will accumulate at the cells. Damage to target cells should be related to the pollutant component involved and its source and dose. Health effects are also closely dependent on country, area, season, and time. An extended exposure duration to the pollutant should incline to long-term health effects in relation also to the above factors.

Particulate Matter (PMs), dust, benzene, and O 3 cause serious damage to the respiratory system ( 97 ). Moreover, there is a supplementary risk in case of existing respiratory disease such as asthma ( 98 ). Long-term effects are more frequent in people with a predisposing disease state. When the trachea is contaminated by pollutants, voice alterations may be remarked after acute exposure. Chronic obstructive pulmonary disease (COPD) may be induced following air pollution, increasing morbidity and mortality ( 99 ). Long-term effects from traffic, industrial air pollution, and combustion of fuels are the major factors for COPD risk ( 99 ).

Multiple cardiovascular effects have been observed after exposure to air pollutants ( 100 ). Changes occurred in blood cells after long-term exposure may affect cardiac functionality. Coronary arteriosclerosis was reported following long-term exposure to traffic emissions ( 101 ), while short-term exposure is related to hypertension, stroke, myocardial infracts, and heart insufficiency. Ventricle hypertrophy is reported to occur in humans after long-time exposure to nitrogen oxide (NO 2 ) ( 102 , 103 ).

Neurological effects have been observed in adults and children after extended-term exposure to air pollutants.

Psychological complications, autism, retinopathy, fetal growth, and low birth weight seem to be related to long-term air pollution ( 83 ). The etiologic agent of the neurodegenerative diseases (Alzheimer's and Parkinson's) is not yet known, although it is believed that extended exposure to air pollution seems to be a factor. Specifically, pesticides and metals are cited as etiological factors, together with diet. The mechanisms in the development of neurodegenerative disease include oxidative stress, protein aggregation, inflammation, and mitochondrial impairment in neurons ( 104 ) ( Figure 1 ).

Impact of air pollutants on the brain.

Brain inflammation was observed in dogs living in a highly polluted area in Mexico for a long period ( 105 ). In human adults, markers of systemic inflammation (IL-6 and fibrinogen) were found to be increased as an immediate response to PNC on the IL-6 level, possibly leading to the production of acute-phase proteins ( 106 ). The progression of atherosclerosis and oxidative stress seem to be the mechanisms involved in the neurological disturbances caused by long-term air pollution. Inflammation comes secondary to the oxidative stress and seems to be involved in the impairment of developmental maturation, affecting multiple organs ( 105 , 107 ). Similarly, other factors seem to be involved in the developmental maturation, which define the vulnerability to long-term air pollution. These include birthweight, maternal smoking, genetic background and socioeconomic environment, as well as education level.

However, diet, starting from breast-feeding, is another determinant factor. Diet is the main source of antioxidants, which play a key role in our protection against air pollutants ( 108 ). Antioxidants are free radical scavengers and limit the interaction of free radicals in the brain ( 108 ). Similarly, genetic background may result in a differential susceptibility toward the oxidative stress pathway ( 60 ). For example, antioxidant supplementation with vitamins C and E appears to modulate the effect of ozone in asthmatic children homozygous for the GSTM1 null allele ( 61 ). Inflammatory cytokines released in the periphery (e.g., respiratory epithelia) upregulate the innate immune Toll-like receptor 2. Such activation and the subsequent events leading to neurodegeneration have recently been observed in lung lavage in mice exposed to ambient Los Angeles (CA, USA) particulate matter ( 61 ). In children, neurodevelopmental morbidities were observed after lead exposure. These children developed aggressive and delinquent behavior, reduced intelligence, learning difficulties, and hyperactivity ( 109 ). No level of lead exposure seems to be “safe,” and the scientific community has asked the Centers for Disease Control and Prevention (CDC) to reduce the current screening guideline of 10 μg/dl ( 109 ).

It is important to state that impact on the immune system, causing dysfunction and neuroinflammation ( 104 ), is related to poor air quality. Yet, increases in serum levels of immunoglobulins (IgA, IgM) and the complement component C3 are observed ( 106 ). Another issue is that antigen presentation is affected by air pollutants, as there is an upregulation of costimulatory molecules such as CD80 and CD86 on macrophages ( 110 ).

As is known, skin is our shield against ultraviolet radiation (UVR) and other pollutants, as it is the most exterior layer of our body. Traffic-related pollutants, such as PAHs, VOCs, oxides, and PM, may cause pigmented spots on our skin ( 111 ). On the one hand, as already stated, when pollutants penetrate through the skin or are inhaled, damage to the organs is observed, as some of these pollutants are mutagenic and carcinogenic, and, specifically, they affect the liver and lung. On the other hand, air pollutants (and those in the troposphere) reduce the adverse effects of ultraviolet radiation UVR in polluted urban areas ( 111 ). Air pollutants absorbed by the human skin may contribute to skin aging, psoriasis, acne, urticaria, eczema, and atopic dermatitis ( 111 ), usually caused by exposure to oxides and photochemical smoke ( 111 ). Exposure to PM and cigarette smoking act as skin-aging agents, causing spots, dyschromia, and wrinkles. Lastly, pollutants have been associated with skin cancer ( 111 ).

Higher morbidity is reported to fetuses and children when exposed to the above dangers. Impairment in fetal growth, low birth weight, and autism have been reported ( 112 ).

Another exterior organ that may be affected is the eye. Contamination usually comes from suspended pollutants and may result in asymptomatic eye outcomes, irritation ( 112 ), retinopathy, or dry eye syndrome ( 113 , 114 ).

Environmental Impact of Air Pollution

Air pollution is harming not only human health but also the environment ( 115 ) in which we live. The most important environmental effects are as follows.

Acid rain is wet (rain, fog, snow) or dry (particulates and gas) precipitation containing toxic amounts of nitric and sulfuric acids. They are able to acidify the water and soil environments, damage trees and plantations, and even damage buildings and outdoor sculptures, constructions, and statues.

Haze is produced when fine particles are dispersed in the air and reduce the transparency of the atmosphere. It is caused by gas emissions in the air coming from industrial facilities, power plants, automobiles, and trucks.

Ozone , as discussed previously, occurs both at ground level and in the upper level (stratosphere) of the Earth's atmosphere. Stratospheric ozone is protecting us from the Sun's harmful ultraviolet (UV) rays. In contrast, ground-level ozone is harmful to human health and is a pollutant. Unfortunately, stratospheric ozone is gradually damaged by ozone-depleting substances (i.e., chemicals, pesticides, and aerosols). If this protecting stratospheric ozone layer is thinned, then UV radiation can reach our Earth, with harmful effects for human life (skin cancer) ( 116 ) and crops ( 117 ). In plants, ozone penetrates through the stomata, inducing them to close, which blocks CO 2 transfer and induces a reduction in photosynthesis ( 118 ).

Global climate change is an important issue that concerns mankind. As is known, the “greenhouse effect” keeps the Earth's temperature stable. Unhappily, anthropogenic activities have destroyed this protecting temperature effect by producing large amounts of greenhouse gases, and global warming is mounting, with harmful effects on human health, animals, forests, wildlife, agriculture, and the water environment. A report states that global warming is adding to the health risks of poor people ( 119 ).

People living in poorly constructed buildings in warm-climate countries are at high risk for heat-related health problems as temperatures mount ( 119 ).

Wildlife is burdened by toxic pollutants coming from the air, soil, or the water ecosystem and, in this way, animals can develop health problems when exposed to high levels of pollutants. Reproductive failure and birth effects have been reported.

Eutrophication is occurring when elevated concentrations of nutrients (especially nitrogen) stimulate the blooming of aquatic algae, which can cause a disequilibration in the diversity of fish and their deaths.

Without a doubt, there is a critical concentration of pollution that an ecosystem can tolerate without being destroyed, which is associated with the ecosystem's capacity to neutralize acidity. The Canada Acid Rain Program established this load at 20 kg/ha/yr ( 120 ).

Hence, air pollution has deleterious effects on both soil and water ( 121 ). Concerning PM as an air pollutant, its impact on crop yield and food productivity has been reported. Its impact on watery bodies is associated with the survival of living organisms and fishes and their productivity potential ( 121 ).

An impairment in photosynthetic rhythm and metabolism is observed in plants exposed to the effects of ozone ( 121 ).

Sulfur and nitrogen oxides are involved in the formation of acid rain and are harmful to plants and marine organisms.

Last but not least, as mentioned above, the toxicity associated with lead and other metals is the main threat to our ecosystems (air, water, and soil) and living creatures ( 121 ).

In 2018, during the first WHO Global Conference on Air Pollution and Health, the WHO's General Director, Dr. Tedros Adhanom Ghebreyesus, called air pollution a “silent public health emergency” and “the new tobacco” ( 122 ).

Undoubtedly, children are particularly vulnerable to air pollution, especially during their development. Air pollution has adverse effects on our lives in many different respects.

Diseases associated with air pollution have not only an important economic impact but also a societal impact due to absences from productive work and school.

Despite the difficulty of eradicating the problem of anthropogenic environmental pollution, a successful solution could be envisaged as a tight collaboration of authorities, bodies, and doctors to regularize the situation. Governments should spread sufficient information and educate people and should involve professionals in these issues so as to control the emergence of the problem successfully.

Technologies to reduce air pollution at the source must be established and should be used in all industries and power plants. The Kyoto Protocol of 1997 set as a major target the reduction of GHG emissions to below 5% by 2012 ( 123 ). This was followed by the Copenhagen summit, 2009 ( 124 ), and then the Durban summit of 2011 ( 125 ), where it was decided to keep to the same line of action. The Kyoto protocol and the subsequent ones were ratified by many countries. Among the pioneers who adopted this important protocol for the world's environmental and climate “health” was China ( 3 ). As is known, China is a fast-developing economy and its GDP (Gross Domestic Product) is expected to be very high by 2050, which is defined as the year of dissolution of the protocol for the decrease in gas emissions.

A more recent international agreement of crucial importance for climate change is the Paris Agreement of 2015, issued by the UNFCCC (United Nations Climate Change Committee). This latest agreement was ratified by a plethora of UN (United Nations) countries as well as the countries of the European Union ( 126 ). In this vein, parties should promote actions and measures to enhance numerous aspects around the subject. Boosting education, training, public awareness, and public participation are some of the relevant actions for maximizing the opportunities to achieve the targets and goals on the crucial matter of climate change and environmental pollution ( 126 ). Without any doubt, technological improvements makes our world easier and it seems difficult to reduce the harmful impact caused by gas emissions, we could limit its use by seeking reliable approaches.

Synopsizing, a global prevention policy should be designed in order to combat anthropogenic air pollution as a complement to the correct handling of the adverse health effects associated with air pollution. Sustainable development practices should be applied, together with information coming from research in order to handle the problem effectively.

At this point, international cooperation in terms of research, development, administration policy, monitoring, and politics is vital for effective pollution control. Legislation concerning air pollution must be aligned and updated, and policy makers should propose the design of a powerful tool of environmental and health protection. As a result, the main proposal of this essay is that we should focus on fostering local structures to promote experience and practice and extrapolate these to the international level through developing effective policies for sustainable management of ecosystems.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

IM is employed by the company Delphis S.A. The remaining authors declare that the present review paper was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Case study: Industrial pollution from an ageing pipeline and its impact on local communities

Topic: Monitoring and resolving industrial pollution.

Engineering disciplines: Chemical engineering; Civil engineering; Manufacturing; Mechanical engineering.

Ethical issues: Environment, Health, Public good.

Professional situations: Bribery, Whistleblowing, Corporate social responsibility, Cultural competency.

Educational level: Advanced.

Educational aim: To encourage ethical motivation. Ethical motivation occurs when a person is moved by a moral judgement, or when a moral judgement is a spur to a course of action. 

Learning and teaching notes:

This case requires an engineer to balance multiple competing factors including: economic pressure, environmental sustainability, and human health. It introduces the perspective of corporate social responsibility (CSR) as a lens through which to view the dilemma. In this case study, the engineer must also make decisions that will affect their professional success in a new job and country.  

This case study addresses two of AHEP 4’s themes: The Engineer and Society (acknowledging that engineering activity can have a significant societal impact) and Engineering Practice (the practical application of engineering concepts, tools and professional skills). To map this case study to AHEP outcomes specific to a programme under these themes, access AHEP 4 here and navigate to pages 30-31 and 35-37.

The dilemma in this case is presented in two parts. If desired, a teacher can use Part one in isolation, but Part two develops and complicates the concepts presented in Part one to provide for additional learning. The case allows teachers the option to stop at multiple points for questions and/or activities as desired.

Learners have the opportunity to:

• practise preparing for a business presentation;
• engage in problem definition in order to elicit ethical components of an issue;
• investigate technical components of pipeline design and groundwater pollution;
• evaluate CSR motivations and practices;
• consider risks and obligations related to whistleblowing.

Teachers have the opportunity to:

• highlight professional situations related to working in new countries or companies;
• evaluate students’ ability to present and defend technical decisions;
• address business approaches to CSR;
• integrate technical content on pipelines and flow.

Learning and teaching resources:

• Engineering Council of India: Code of Ethics
• Environmental ethics and poverty
• Sustainability and CSR
• Engineering Council: Guidance on Whistleblowing

Yasin is a pipeline design engineer who has been employed to manage the wastewater pipeline for MMC Textile Company in Gujarat. The company has a rapidly growing business contributing to one of India’s most important industries for employment and export. Yasin was hired through a remote process during the pandemic – he had never been to the industrial site or met his new colleagues in person until he relocated to the country. For 10 years, Yasin worked for the Water Services Regulation Authority in the UK as a wastewater engineer; this is the first time he has been employed by a private company and worked within the textile industry.

The production of textiles results in highly toxic effluent that must be treated and disposed of. A sludge pipeline takes wastewater away from MMC’s factory site and delivers it to a treatment plant downstream. On arrival at MMC, Yasin undertakes an initial inspection of the industrial site and the pipeline. He conducts some testing and measurements, then reviews the company’s documents and specifications related to the pipeline. This pipeline was built 30 years ago when MMC first began operations. In the last five years, MMC has partnered with a fast fashion chain and invested in advanced production technologies, resulting in a 50% increase in its yearly output. Yasin soon realises that as production has increased, the pipeline sometimes carries nearly double its registered capacity. Yasin was hired because MMC’s managers were aware that the pipeline capacity might be stretched and needed his expertise to develop a solution. However, Yasin suspects they are unaware of the real extent of the problem, and is nervous about how they will react to confirmation of this suspicion. Yasin is due to provide an informal verbal report on his initial inspection to the factory managers. This will be his first official business meeting since arriving in India.

Optional STOP for questions and activities:

1. Discussion: Although Yasin is a qualified and experienced engineer, what professional challenges might he encounter at MMC?

2. Discussion: What preparation does Yasin need to make for this informal meeting? What data or evidence should he present?

3. Activity: Role-play Yasin’s first meeting with the factory managers.

4. Activity: Research the environmental effects of textile production and / or India’s policies on textile waste management.

Dilemma – Part one:

At the meeting, Yasin is tasked with developing a menu of proposals to mitigate the problem. The options he puts forward include retrofitting the original pipeline, replacing it with a new one, eliminating the pipeline entirely and focusing on on-site water treatment technology, as well as other solutions. He is directed to consider the risks and benefits of the alternatives. These include the economic burdens, both the cost of the intervention as well as the decline in production necessitated while the intervention takes place, and the environmental consequences of action or inaction.  

During his research, Yasin discovers that informal housing has sprung up in the grey zone between the area’s formal zoned conurbation and the MMC industrial site. This is because there is little local regulation or enforcement as to where people are allowed to erect temporary or permanent dwellings. He estimates that there are several thousand people living in impoverished conditions on the edges of MMC’s property. Indeed, many of the people living in the informal settlement work in the lowest-skilled jobs at the textile factory. The informal settlement is located around a well that Yasin suspects may be polluted by effluent that seeps into the soil and groundwater when the pipeline overflows. He can find no information in company records about data related to this potential pollution.

1. Discussion: Does Yasin have a responsibility to do anything about the potential groundwater pollution at the informal settlement?

2. Discussion: Should Yasin advocate for the solution with the lowest cost?

3. Activity: Practise problem definition. What are the parameters and criteria Yasin should use in defining the issues at stake? What elements of the problem is he technically or ethically obligated to resolve? Why?

4. Activity: Create a tether diagram mapping the effects of each potential solution on the company, the local people, and the environment.

5. Activity: Undertake a technical activity in the areas of chemical, civil, manufacturing and / or mechanical engineering related to groundwater pollution.

Dilemma – Part two:

As Yasin learns more about MMC, he discovers that as the company grew rapidly in the last five years,  and has boosted its CSR initiatives, MMC started a programme to hire and upskill local labourers and began a charitable foundation to make donations to local schools and charities. For these activities, MMC has recently received a government commendation for its community commitments. Yasin is concerned about how to make sense of these activities on the one hand, and the potential groundwater contamination on the other. He speaks to his supervisor about MMC’s CSR initiatives and learns that company directors believe that their commendation will pave the way for an even better relationship with the government and perhaps enable a favourable decision on a permit to build another textile factory site nearby. At the end of the conversation, his supervisor indicates that if a new factory is built, it will need a chief site engineer. “That position would be double your current salary,” the supervisor says, “a good job on fixing this pipeline situation would make you look like a very attractive candidate.” Yasin is due to formally present his proposal about the pipeline next week to the factory manager and company directors.

1. Discussion: How should Yasin respond to the suggestion of a job offer?

2. Discussion: Should Yasin report any of MMC’s actions or motivations to an external authority?

3. Activity: Research CSR and its ethical dimensions, both in the UK and in India.

4. Activity: Undertake a technical activity in the areas of chemical, civil, manufacturing and / or mechanical engineering, related to pipeline design and flow rates.

5. Activity: Debate whether or not Yasin should become a whistleblower, either about the groundwater pollution or the job offer.

Enhancements:

An enhancement for this case study can be found here .

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License .

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.

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• Published: 29 October 2020

Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends

• Lu Liang 1 &
• Peng Gong 2 , 3 , 4  

Scientific Reports volume  10 , Article number:  18618 ( 2020 ) Cite this article

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Most air pollution research has focused on assessing the urban landscape effects of pollutants in megacities, little is known about their associations in small- to mid-sized cities. Considering that the biggest urban growth is projected to occur in these smaller-scale cities, this empirical study identifies the key urban form determinants of decadal-long fine particulate matter (PM 2.5 ) trends in all 626 Chinese cities at the county level and above. As the first study of its kind, this study comprehensively examines the urban form effects on air quality in cities of different population sizes, at different development levels, and in different spatial-autocorrelation positions. Results demonstrate that the urban form evolution has long-term effects on PM 2.5 level, but the dominant factors shift over the urbanization stages: area metrics play a role in PM 2.5 trends of small-sized cities at the early urban development stage, whereas aggregation metrics determine such trends mostly in mid-sized cities. For large cities exhibiting a higher degree of urbanization, the spatial connectedness of urban patches is positively associated with long-term PM 2.5 level increases. We suggest that, depending on the city’s developmental stage, different aspects of the urban form should be emphasized to achieve long-term clean air goals.

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Introduction.

Air pollution represents a prominent threat to global society by causing cascading effects on individuals 1 , medical systems 2 , ecosystem health 3 , and economies 4 in both developing and developed countries 5 , 6 , 7 , 8 . About 90% of global citizens lived in areas that exceed the safe level in the World Health Organization (WHO) air quality guidelines 9 . Among all types of ecosystems, urban produce roughly 78% of carbon emissions and substantial airborne pollutants that adversely affect over 50% of the world’s population living in them 5 , 10 . While air pollution affects all regions, there exhibits substantial regional variation in air pollution levels 11 . For instance, the annual mean concentration of fine particulate matter with an aerodynamic diameter of less than 2.5  \(\upmu\mathrm{m}\) (PM 2.5 ) in the most polluted cities is nearly 20 times higher than the cleanest city according to a survey of 499 global cities 12 . Many factors can influence the regional air quality, including emissions, meteorology, and physicochemical transformations. Another non-negligible driver is urbanization—a process that alters the size, structure, and growth of cities in response to the population explosion and further leads to lasting air quality challenges 13 , 14 , 15 .

With the global trend of urbanization 16 , the spatial composition, configuration, and density of urban land uses (refer to as urban form) will continue to evolve 13 . The investigation of urban form impacts on air quality has been emerging in both empirical 17 and theoretical 18 research. While the area and density of artificial surface areas have well documented positive relationship with air pollution 19 , 20 , 21 , the effects of urban fragmentation on air quality have been controversial. In theory, compact cities promote high residential density with mixed land uses and thus reduce auto dependence and increase the usage of public transit and walking 21 , 22 . The compact urban development has been proved effective in mitigating air pollution in some cities 23 , 24 . A survey of 83 global urban areas also found that those with highly contiguous built-up areas emitted less NO 2 22 . In contrast, dispersed urban form can decentralize industrial polluters, improve fuel efficiency with less traffic congestion, and alleviate street canyon effects 25 , 26 , 27 , 28 . Polycentric and dispersed cities support the decentralization of jobs that lead to less pollution emission than compact and monocentric cities 29 . The more open spaces in a dispersed city support air dilution 30 . In contrast, compact cities are typically associated with stronger urban heat island effects 31 , which influence the availability and the advection of primary and secondary pollutants 32 .

The mixed evidence demonstrates the complex interplay between urban form and air pollution, which further implies that the inconsistent relationship may exist in cities at different urbanization levels and over different periods 33 . Few studies have attempted to investigate the urban form–air pollution relationship with cross-sectional and time series data 34 , 35 , 36 , 37 . Most studies were conducted in one city or metropolitan region 38 , 39 or even at the country level 40 . Furthermore, large cities or metropolitan areas draw the most attention in relevant studies 5 , 41 , 42 , and the small- and mid-sized cities, especially those in developing countries, are heavily underemphasized. However, virtually all world population growth 43 , 44 and most global economic growth 45 , 46 are expected to occur in those cities over the next several decades. Thus, an overlooked yet essential task is to account for various levels of cities, ranging from large metropolitan areas to less extensive urban area, in the analysis.

This study aims to improve the understanding of how the urban form evolution explains the decadal-long changes of the annual mean PM 2.5 concentrations in 626 cities at the county-level and above in China. China has undergone unprecedented urbanization over the past few decades and manifested a high degree of heterogeneity in urban development 47 . Thus, Chinese cities serve as a good model for addressing the following questions: (1) whether the changes in urban landscape patterns affect trends in PM 2.5 levels? And (2) if so, do the determinants vary by cities?

City boundaries

Our study period spans from the year 2000 to 2014 to keep the data completeness among all data sources. After excluding cities with invalid or missing PM 2.5 or sociodemographic value, a total of 626 cities, with 278 prefecture-level cities and 348 county-level cities, were selected. City boundaries are primarily based on the Global Rural–Urban Mapping Project (GRUMP) urban extent polygons that were defined by the extent of the nighttime lights 48 , 49 . Few adjustments were made. First, in the GRUMP dataset, large agglomerations that include several cities were often described in one big polygon. We manually split those polygons into individual cities based on the China Administrative Regions GIS Data at 1:1 million scales 50 . Second, since the 1978 economic reforms, China has significantly restructured its urban administrative/spatial system. Noticeable changes are the abolishment of several prefectures and the promotion of many former county-level cities to prefecture-level cities 51 . Thus, all city names were cross-checked between the year 2000 and 2014, and the mismatched records were replaced with the latest names.

PM 2.5 concentration data

The annual mean PM 2.5 surface concentration (micrograms per cubic meter) for each city over the study period was calculated from the Global Annual PM 2.5 Grids at 0.01° resolution 52 . This data set combines Aerosol Optical Depth retrievals from multiple satellite instruments including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The global 3-D chemical transport model GEOS-Chem is further applied to relate this total column measure of aerosol to near-surface PM 2.5 concentration, and geographically weighted regression is finally used with global ground-based measurements to predict and adjust for the residual PM 2.5 bias per grid cell in the initial satellite-derived values.

Human settlement layer

The urban forms were quantified with the 40-year (1978–2017) record of annual impervious surface maps for both rural and urban areas in China 47 , 53 . This state-of-art product provides substantial spatial–temporal details on China’s human settlement changes. The annual impervious surface maps covering our study period were generated from 30-m resolution Landsat images acquired onboard Landsat 5, 7, and 8 using an automatic “Exclusion/Inclusion” mapping framework 54 , 55 . The output used here was the binary impervious surface mask, with the value of one indicating the presence of human settlement and the value of zero identifying non-residential areas. The product assessment concluded good performance. The cross-comparison against 2356 city or town locations in GeoNames proved an overall high agreement (88%) and approximately 80% agreement was achieved when compared against visually interpreted 650 urban extent areas in the year 1990, 2000, and 2010.

Control variables

To provide a holistic assessment of the urban form effects, we included control variables that are regarded as important in influencing air quality to account for the confounding effects.

Four variables, separately population size, population density, and two economic measures, were acquired from the China City Statistical Yearbook 56 (National Bureau of Statistics 2000–2014). Population size is used to control for the absolute level of pollution emissions 41 . Larger populations are associated with increased vehicle usage and vehicle-kilometers travels, and consequently boost tailpipes emissions 5 . Population density is a useful reflector of transportation demand and the fraction of emissions inhaled by people 57 . We also included gross regional product (GRP) and the proportion of GRP generated from the secondary sector (GRP2). The impact of economic development on air quality is significant but in a dynamic way 58 . The rising per capita income due to the concentration of manufacturing industrial activities can deteriorate air quality and vice versa if the stronger economy is the outcome of the concentration of less polluting high-tech industries. Meteorological conditions also have short- and long-term effects on the occurrence, transport, and dispersion of air pollutants 59 , 60 , 61 . Temperature affects chemical reactions and atmospheric turbulence that determine the formation and diffusion of particles 62 . Low air humidity can lead to the accumulation of air pollutants due to it is conducive to the adhesion of atmospheric particulate matter on water vapor 63 . Whereas high humidity can lead to wet deposition processes that can remove air pollutants by rainfall. Wind speed is a crucial indicator of atmospheric activity by greatly affect air pollutant transport and dispersion. All meteorological variables were calculated based on China 1 km raster layers of monthly relative humidity, temperature, and wind speed that are interpolated from over 800 ground monitoring stations 64 . Based on the monthly layer, we calculated the annual mean of each variable for each year. Finally, all pixels falling inside of the city boundary were averaged to represent the overall meteorological condition of each city.

Considering the dynamic urban form-air pollution relationship evidenced from the literature review, our hypothesis is: the determinants of PM 2.5 level trends are not the same for cities undergoing different levels of development or in different geographic regions. To test this hypothesis, we first categorized city groups following (1) social-economic development level, (2) spatial autocorrelation relationship, and (3) population size. We then assessed the relationship between urban form and PM 2.5 level trends by city groups. Finally, we applied the panel data models to different city groups for hypothesis testing and key determinant identification (Fig.  1 ).

Methodology workflow.

Calculation of urban form metrics

Based on the previous knowledge 65 , 66 , 67 , fifteen landscape metrics falling into three categories, separately area, shape, and aggregation, were selected. Those metrics quantify the compositional and configurational characteristics of the urban landscape, as represented by urban expansion, urban shape complexity, and compactness (Table 1 ).

Area metrics gives an overview of the urban extent and the size of urban patches that are correlated with PM 2.5 20 . As an indicator of the urbanization degree, total area (TA) typically increases constantly or remains stable, because the urbanization process is irreversible. Number of patches (NP) refers to the number of discrete parcels of urban settlement within a given urban extent and Mean Patch Size (AREA_MN) measures the average patch size. Patch density (PD) indicates the urbanization stages. It usually increases with urban diffusion until coalescence starts, after which decreases in number 66 . Largest Patch Index (LPI) measures the percentage of the landscape encompassed by the largest urban patch.

The shape complexity of urban patches was represented by Mean Patch Shape Index (SHAPE_MN), Mean Patch Fractal Dimension (FRAC_MN), and Mean Contiguity Index (CONTIG_MN). The greater irregularity the landscape shape, the larger the value of SHAPE_MN and FRAC_MN. CONTIG_MN is another method of assessing patch shape based on the spatial connectedness or contiguity of cells within a patch. Larger contiguous patches will result in larger CONTIG_MN.

Aggregation metrics measure the spatial compactness of urban land, which affects pollutant diffusion and dilution. Mean Euclidean nearest-neighbor distance (ENN_MN) quantifies the average distance between two patches within a landscape. It decreases as patches grow together and increases as the urban areas expand. Landscape Shape Index (LSI) indicates the divergence of the shape of a landscape patch that increases as the landscape becomes increasingly disaggregated 68 . Patch Cohesion Index (COHESION) is suggestive of the connectedness degree of patches 69 . Splitting Index (SPLIT) and Landscape Division Index (DIVISION) increase as the separation of urban patches rises, whereas, Mesh Size (MESH) decreases as the landscape becomes more fragmented. Aggregation Index (AI) measures the degree of aggregation or clumping of urban patches. Higher values of continuity indicate higher building densities, which may have a stronger effect on pollution diffusion.

The detailed descriptions of these indices are given by the FRAGSTATS user’s guide 70 . The calculation input is a layer of binary grids of urban/nonurban. The resulting output is a table containing one row for each city and multiple columns representing the individual metrics.

Division of cities

Division based on the socioeconomic development level.

The socioeconomic development level in China is uneven. The unequal development of the transportation system, descending in topography from the west to the east, combined with variations in the availability of natural and human resources and industrial infrastructure, has produced significantly wide gaps in the regional economies of China. By taking both the economic development level and natural geography into account, China can be loosely classified into Eastern, Central, and Western regions. Eastern China is generally wealthier than the interior, resulting from closeness to coastlines and the Open-Door Policy favoring coastal regions. Western China is historically behind in economic development because of its high elevation and rugged topography, which creates barriers in the transportation infrastructure construction and scarcity of arable lands. Central China, echoing its name, is in the process of economic development. This region neither benefited from geographic convenience to the coast nor benefited from any preferential policies, such as the Western Development Campaign.

Division based on spatial autocorrelation relationship

The second type of division follows the fact that adjacent cities are likely to form air pollution clusters due to the mixing and diluting nature of air pollutants 71 , i.e., cities share similar pollution levels as its neighbors. The underlying processes driving the formation of pollution hot spots and cold spots may differ. Thus, we further divided the city into groups based on the spatial clusters of PM 2.5 level changes.

Local indicators of spatial autocorrelation (LISA) was used to determine the local patterns of PM 2.5 distribution by clustering cities with a significant association. In the presence of global spatial autocorrelation, LISA indicates whether a variable exhibits significant spatial dependence and heterogeneity at a given scale 72 . Practically, LISA relates each observation to its neighbors and assigns a value of significance level and degree of spatial autocorrelation, which is calculated by the similarity in variable \(z\) between observation \(i\) and observation \(j\) in the neighborhood of \(i\) defined by a matrix of weights \({w}_{ij}\) 7 , 73 :

where \({I}_{i}\) is the Moran’s I value for location \(i\) ; \({\sigma }^{2}\) is the variance of variable \(z\) ; \(\bar{z}\) is the average value of \(z\) with the sample number of \(n\) . The weight matrix \({w}_{ij}\) is defined by the k-nearest neighbors distance measure, i.e., each object’s neighborhood consists of four closest cites.

The computation of Moran’s I enables the identification of hot spots and cold spots. The hot spots are high-high clusters where the increase in the PM 2.5 level is higher than the surrounding areas, whereas cold spots are low-low clusters with the presence of low values in a low-value neighborhood. A Moran scatterplot, with x-axis as the original variable and y-axis as the spatially lagged variable, reflects the spatial association pattern. The slope of the linear fit to the scatter plot is an estimation of the global Moran's I 72 (Fig.  2 ). The plot consists of four quadrants, each defining the relationship between an observation 74 . The upper right quadrant indicates hot spots and the lower left quadrant displays cold spots 75 .

Moran’s I scatterplot. Figure was produced by R 3.4.3 76 .

Division based on population size

The last division was based on population size, which is a proven factor in changing per capita emissions in a wide selection of global cities, even outperformed land urbanization rate 77 , 78 , 79 . We used the 2014 urban population to classify the cities into four groups based on United Nations definitions 80 : (1) large agglomerations with a total population larger than 1 million; (2) mid-sized cities, 500,000–1 million; (3) small cities, 250,000–500,000, and (4) very small cities, 100,000–250,000.

Panel data analysis

The panel data analysis is an analytical method that deals with observations from multiple entities over multiple periods. Its capacity in analyzing the characteristics and changes from both the time-series and cross-section dimensions of data surpasses conventional models that purely focus on one dimension 81 , 82 . The estimation equation for the panel data model in this study is given as:

where the subscript \(i\) and \(t\) refer to city and year respectively. \(\upbeta _{{0}}\) is the intercept parameter and \(\upbeta _{{1}} - { }\upbeta _{{{18}}}\) are the estimates of slope coefficients. \(\varepsilon \) is the random error. All variables are transformed into natural logarithms.

Two methods can be used to obtain model estimates, separately fixed effects estimator and random effects estimator. The fixed effects estimator assumes that each subject has its specific characteristics due to inherent individual characteristic effects in the error term, thereby allowing differences to be intercepted between subjects. The random effects estimator assumes that the individual characteristic effect changes stochastically, and the differences in subjects are not fixed in time and are independent between subjects. To choose the right estimator, we run both models for each group of cities based on the Hausman specification test 83 . The null hypothesis is that random effects model yields consistent and efficient estimates 84 : \({H}_{0}{:}\,E\left({\varepsilon }_{i}|{X}_{it}\right)=0\) . If the null hypothesis is rejected, the fixed effects model will be selected for further inferences. Once the better estimator was determined for each model, one optimal panel data model was fit to each city group of one division type. In total, six, four, and eight runs were conducted for socioeconomic, spatial autocorrelation, and population division separately and three, two, and four panel data models were finally selected.

Spatial patterns of PM 2.5 level changes

During the period from 2000 to 2014, the annual mean PM 2.5 concentration of all cities increases from 27.78 to 42.34 µg/m 3 , both of which exceed the World Health Organization recommended annual mean standard (10 µg/m 3 ). It is worth noting that the PM 2.5 level in the year 2014 also exceeds China’s air quality Class 2 standard (35 µg/m 3 ) that applies to non-national park places, including urban and industrial areas. The standard deviation of annual mean PM 2.5 values for all cities increases from 12.34 to 16.71 µg/m 3 , which shows a higher variability of inter-urban PM 2.5 pollution after a decadal period. The least and most heavily polluted cities in China are Delingha, Qinghai (3.01 µg/m 3 ) and Jizhou, Hubei (64.15 µg/m 3 ) in 2000 and Hami, Xinjiang (6.86 µg/m 3 ) and Baoding, Hubei (86.72 µg/m 3 ) in 2014.

Spatially, the changes in PM 2.5 levels exhibit heterogeneous patterns across cities (Fig.  3 b). According to the socioeconomic level division (Fig.  3 a), the Eastern, Central, and Western region experienced a 38.6, 35.3, and 25.5 µg/m 3 increase in annual PM 2.5 mean , separately, and the difference among regions is significant according to the analysis of variance (ANOVA) results (Fig.  4 a). When stratified by spatial autocorrelation relationship (Fig.  3 c), the differences in PM 2.5 changes among the spatial clusters are even more dramatic. The average PM 2.5 increase in cities belonging to the high-high cluster is approximately 25 µg/m 3 , as compared to 5 µg/m 3 in the low-low clusters (Fig.  4 b). Finally, cities at four different population levels have significant differences in the changes of PM 2.5 concentration (Fig.  3 d), except for the mid-sized cities and large city agglomeration (Fig.  4 c).

( a ) Division of cities in China by socioeconomic development level and the locations of provincial capitals; ( b ) Changes in annual mean PM 2.5 concentrations between the year 2000 and 2014; ( c ) LISA cluster maps for PM 2.5 changes at the city level; High-high indicates a statistically significant cluster of high PM 2.5 level changes over the study period. Low-low indicates a cluster of low PM 2.5 inter-annual variation; No high-low cluster is reported; Low–high represents cities with high PM 2.5 inter-annual variation surrounded by cities with low variation; ( d ) Population level by cities in the year 2014. Maps were produced by ArcGIS 10.7.1 85 .

Boxplots of PM 2.5 concentration changes between 2000 and 2014 for city groups that are formed according to ( a ) socioeconomic development level division, ( b ) LISA clusters, and ( c ) population level. Asterisk marks represent the p value of ANOVA significant test between the corresponding pair of groups. Note ns not significant; * p value < 0.05; ** p value < 0.01; *** p value < 0.001; H–H high-high cluster, L–H low–high cluster, L–L denotes low–low cluster.

The effects of urban forms on PM 2.5 changes

The Hausman specification test for fixed versus random effects yields a p value less than 0.05, suggesting that the fixed effects model has better performance. We fit one panel data model to each city group and built nine models in total. All models are statistically significant at the p  < 0.05 level and have moderate to high predictive power with the R 2 values ranging from 0.63 to 0.95, which implies that 63–95% of the variation in the PM 2.5 concentration changes can be explained by the explanatory variables (Table 2 ).

The urban form—PM 2.5 relationships differ distinctly in Eastern, Central, and Western China. All models reach high R 2 values. Model for Eastern China (refer to hereafter as Eastern model) achieves the highest R 2 (0.90), and the model for the Western China (refer to hereafter as Western model) reaches the lowest R 2 (0.83). The shape metrics FRAC and CONTIG are correlated with PM 2.5 changes in the Eastern model, whereas the area metrics AREA demonstrates a positive effect in the Western model. In contrast to the significant associations between shape, area metrics and PM 2.5 level changes in both Eastern and Western models, no such association was detected in the Central model. Nonetheless, two aggregation metrics, LSI and AI, play positive roles in determining the PM 2.5 trends in the Central model.

For models built upon the LISA clusters, the H–H model (R 2  = 0.95) reaches a higher fitting degree than the L–L model (R 2  = 0.63). The estimated coefficients vary substantially. In the H–H model, the coefficient of CONTIG is positive, which indicates that an increase in CONTIG would increase PM 2.5 pollution. In contrast, no shape metrics but one area metrics AREA is significant in the L–L model.

The results of the regression models built for cities at different population levels exhibit a distinct pattern. No urban form metrics was identified to have a significant relationship with the PM 2.5 level changes in groups of very small and mid-sized cities. For small size cities, the aggregation metrics COHESION was positively associated whereas AI was negatively related. For mid-sized cities and large agglomerations, CONTIG is the only significant variable that is positively related to PM 2.5 level changes.

Urban form is an effective measure of long-term PM 2.5 trends

All panel data models are statistically significant regardless of the data group they are built on, suggesting that the associations between urban form and ambient PM 2.5 level changes are discernible at all city levels. Importantly, these relationships are found to hold when controlling for population size and gross domestic product, implying that the urban landscape patterns have effects on long-term PM 2.5 trends that are independent of regional economic performance. These findings echo with the local, regional, and global evidence of urban form effect on various air pollution types 5 , 14 , 21 , 22 , 24 , 39 , 78 .

Although all models demonstrate moderate to high predictive power, the way how different urban form metrics respond to the dependent variable varies. Of all the metrics tested, shape metrics, especially CONTIG has the strongest effect on PM 2.5 trends in cities belonging to the high-high cluster, Eastern, and large urban agglomerations. All those regions have a strong economy and higher population density 86 . In the group of cities that are moderately developed, such as the Central region, as well as small- and mid-sized cities, aggregation metrics play a dominant negative role in PM 2.5 level changes. In contrast, in the least developed cities belonging to the low-low cluster regions and Western China, the metrics describing size and number of urban patches are the strongest predictors. AREA and NP are positively related whereas TA is negatively associated.

The impacts of urban form metrics on air quality vary by urbanization degree

Based on the above observations, how urban form affects within-city PM 2.5 level changes may differ over the urbanization stages. We conceptually summarized the pattern in Fig.  5 : area metrics have the most substantial influence on air pollution changes at the early urban development stage, and aggregation metrics emerge at the transition stage, whereas shape metrics affect the air quality trends at the terminal stage. The relationship between urban form and air pollution has rarely been explored with such a wide range of city selections. Most prior studies were focused on large urban agglomeration areas, and thus their conclusions are not representative towards small cities at the early or transition stage of urbanization.

The most influential metric of urban form in affecting PM 2.5 level changes at different urbanization stages.

Not surprisingly, the area metrics, which describe spatial grain of the landscape, exert a significant effect on PM 2.5 level changes in small-sized cities. This could be explained by the unusual urbanization speed of small-sized cities in the Chinese context. Their thriving mostly benefited from the urbanization policy in the 1980s, which emphasized industrialization of rural, small- and mid-sized cities 87 . With the large rural-to-urban migration and growing public interest in investing real estate market, a side effect is that the massive housing construction that sometimes exceeds market demand. Residential activities decline in newly built areas of smaller cities in China, leading to what are known as ghost cities 88 . Although ghost cities do not exist for all cities, high rate of unoccupied dwellings is commonly seen in cities under the prefectural level. This partly explained the negative impacts of TA on PM 2.5 level changes, as an expanded while unoccupied or non-industrialized urban zones may lower the average PM 2.5 concentration within the city boundary, but it doesn’t necessarily mean that the air quality got improved in the city cores.

Aggregation metrics at the landscape scale is often referred to as landscape texture that quantifies the tendency of patch types to be spatially aggregated; i.e., broadly speaking, aggregated or “contagious” distributions. This group of metrics is most effective in capturing the PM 2.5 trends in mid-sized cities (population range 25–50 k) and Central China, where the urbanization process is still undergoing. The three significant variables that reflect the spatial property of dispersion, separately landscape shape index, patch cohesion index, and aggregation index, consistently indicate that more aggregated landscape results in a higher degree of PM 2.5 level changes. Theoretically, the more compact urban form typically leads to less auto dependence and heavier reliance on the usage of public transit and walking, which contributes to air pollution mitigation 89 . This phenomenon has also been observed in China, as the vehicle-use intensity (kilometers traveled per vehicle per year, VKT) has been declining over recent years 90 . However, VKT only represents the travel intensity of one car and does not reflect the total distance traveled that cumulatively contribute to the local pollution. It should be noted that the private light-duty vehicle ownership in China has increased exponentially and is forecast to reach 23–42 million by 2050, with the share of new-growth purchases representing 16–28% 90 . In this case, considering the increased total distance traveled, the less dispersed urban form can exert negative effects on air quality by concentrating vehicle pollution emissions in a limited space.

Finally, urban contiguity, observed as the most effective shape metric in indicating PM 2.5 level changes, provides an assessment of spatial connectedness across all urban patches. Urban contiguity is found to have a positive effect on the long-term PM 2.5 pollution changes in large cities. Urban contiguity reflects to which degree the urban landscape is fragmented. Large contiguous patches result in large CONTIG_MN values. Among the 626 cities, only 11% of cities experience negative changes in urban contiguity. For example, Qingyang, Gansu is one of the cities-featuring leapfrogs and scattered development separated by vacant land that may later be filled in as the development continues (Fig.  6 ). Most Chinese cities experienced increased urban contiguity, with less fragmented and compacted landscape. A typical example is Shenzhou, Hebei, where CONTIG_MN rose from 0.27 to 0.45 within the 14 years. Although the 13 counties in Shenzhou are very far scattered from each other, each county is growing intensively internally rather than sprawling further outside. And its urban layout is thus more compact (Fig.  6 ). The positive association revealed in this study contradicts a global study indicating that cities with highly contiguous built-up areas have lower NO 2 pollution 22 . We noticed that the principal emission sources of NO 2 differ from that of PM 2.5. NO 2 is primarily emitted with the combustion of fossil fuels (e.g., industrial processes and power generation) 6 , whereas road traffic attributes more to PM 2.5 emissions. Highly connected urban form is likely to cause traffic congestion and trap pollution inside the street canyon, which accumulates higher PM 2.5 concentration. Computer simulation results also indicate that more compact cities improve urban air quality but are under the premise that mixed land use should be presented 18 . With more connected impervious surfaces, it is merely impossible to expect increasing urban green spaces. If compact urban development does not contribute to a rising proportion of green areas, then such a development does not help mitigating air pollution 41 .

Six cities illustrating negative to positive changes in CONTIG_MN and AREA_MN. Pixels in black show the urban areas in the year 2000 and pixels in red are the expanded urban areas from the year 2000 to 2014. Figure was produced by ArcGIS 10.7.1 85 .

Conclusions

This study explores the regional land-use patterns and air quality in a country with an extraordinarily heterogeneous urbanization pattern. Our study is the first of its kind in investigating such a wide range selection of cities ranging from small-sized ones to large metropolitan areas spanning a long time frame, to gain a comprehensive insight into the varying effects of urban form on air quality trends. And the primary insight yielded from this study is the validation of the hypothesis that the determinants of PM 2.5 level trends are not the same for cities at various developmental levels or in different geographic regions. Certain measures of urban form are robust predictors of air quality trends for a certain group of cities. Therefore, any planning strategy aimed at reducing air pollution should consider its current development status and based upon which, design its future plan. To this end, it is also important to emphasize the main shortcoming of this analysis, which is generally centered around the selection of control variables. This is largely constrained by the available information from the City Statistical Yearbook. It will be beneficial to further polish this study by including other important controlling factors, such as vehicle possession.

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Acknowledgements

Lu Liang received intramural research funding support from the UNT Office of Research and Innovation. Peng Gong is partially supported by the National Research Program of the Ministry of Science and Technology of the People’s Republic of China (2016YFA0600104), and donations from Delos Living LLC and the Cyrus Tang Foundation to Tsinghua University.

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Liang, L., Gong, P. Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. Sci Rep 10 , 18618 (2020). https://doi.org/10.1038/s41598-020-74524-9

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DOI : https://doi.org/10.1038/s41598-020-74524-9

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Industrial Air Pollution: A case study on Bawana Industrial Area

Industrial emissions are a major source of respirable particulate matter, especially in urban and metropolitan areas. The main pollutants associated with industrial pollution are toxic heavy metals, volatile organic carbon (VOC’s), polyaromatic hydrocarbons (PAH’s), PM10 and PM2.5 which can have serious repercussions on not just human health but also on the environment as a whole. Currently, there are several approaches to consider these missions.However, the uncertainty of the quantification of these emissions is very high. Hence it is necessary to assess the quality of the existing emission factors in order to improve them as well as to verify them. Moreover, it is a well-known fact that the impacts and effects of industrial pollution have been more pronounced in cities of developing countries due to lack of significant advancement in technologies pertaining to air pollution. This work provides an in-depth analysis of the composition of the various aforementioned pollutants involved in industrial air pollution. It also urges the environmental authorities to closely monitor these hazardous sources of pollution as well as consider the possibility of narrowing the emission standard limits set for industries, and at the same time encourage the scientific community to improve existing methods to estimate and validate these emissions.

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• 1 Struttura Tematica di Epidemiologia Ambientale - ARPA Emilia-Romagna, viale Fontanelli 23, 41100 Modena. [email protected]
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Industrial Pollution and Soil Quality—A Case Study from Industrial Area, Visakhapatnam, Andhra Pradesh, India

• First Online: 26 May 2022

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• Pushpanjali 8 ,
• K. L. Sharma 8 ,
• K. Venkanna 8 ,
• Josily Samuel 8 &
• G. Ravindra Chary 8  

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Environmental vis-à-vis industrial pollution is a widespread problem that influences both human health and agricultural productivity. Rapid industrialization leads to substantial increase in the generation of industrial wastes leading to contamination of air, water, and soil. The contamination of soils by heavy metals is a significant problem as it negatively influences soil characteristics which in turn influence crop productivity and food quality. In present study, we focused on the characterization of some physical, chemical, and biological parameters of soil for soil quality assessment in agricultural fields near to Hindustan Zinc Limited (HZL) in Mindi area, which is one of the most polluted areas in Vishakhapatnam, India. As paddy is the major field crop grown in this area, cropped and corresponding adjacent fallow area with and without exposure to effluents were selected. Soil samples were collected from different locations, i.e., in paddy growing site exposed to effluents (PGSEE), in Paddy growing site without exposure to effluents (PGSWE), Fallow Lands without exposure to effluents (FWE), and Fallow lands exposed to effluents (FEE) were collected from Mindi area of Visakhapatnam. All soils were observed with low N, low P, and medium K status except PGSWE. FEE were highly concentrated (mg kg −1 ) with in Cu (7.8), Zn (15.5), Pb (87.8), and Cd (1.8) as compared to paddy cultivated fields. Dehydrogenase activity, i.e., mg Triphenylformazan (TPF) formed 24 h −1  g −1 of soil ranged from 0.54 in FEE–2.31 in PGSWE whereas, Soil microbial biomass Carbon (SMBC) values were in the range of 139.5 (PGSEE)–194.5 (PGSWE) µg g −1 of soil. Results show that the soil quality of both fallow and agriculture land near Hindustan Zinc limited are severely affected by the effluent coming out of the industry and require immediate attention from the people living in this area.

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Pushpanjali, Sharma, K.L., Venkanna, K., Samuel, J., Ravindra Chary, G. (2022). Industrial Pollution and Soil Quality—A Case Study from Industrial Area, Visakhapatnam, Andhra Pradesh, India. In: Mishra, R.K., Kumari, C.L., Chachra, S., Krishna, P.S.J., Dubey, A., Singh, R.B. (eds) Smart Cities for Sustainable Development. Advances in Geographical and Environmental Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-7410-5_20

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