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Abstract: This paper tests whether adopting the international norm ISO 14001 significantly impacts environmental performance in Quebec''s pulp and paper industry. Using monthly data collected from 37 plants between 1997 and 2003, we show that: (i) ISO certification does not lead to a reduction in total suspended solid emissions or in quantity of rejected process water; (ii) discharge of biological oxygen demand decreases by about 9% following certification; (iii) contrary to the group of plants that did not adopt the ISO norm, the adopting plants did not experience a significant negative trend in emissions over our sample period. Moreover, our results show that the impact of ISO is very variable across adopting plants. If some plants considerably reduce emissions following certification, we find that most adopters either maintain or even increase emissions after being ISO accredited. [Copyright &y& Elsevier]

Abstract: This paper discusses the relationship between environmental and economic performance and the influence of corporate strategies with regard to sustainability and the environment. After formulating a theoretical model, results are reported from an empirical analysis of the European paper manufacturing industry. New data are used to test hypotheses derived from the theoretical model, using environmental performance indices representing different corporate environmental strategy orientations. In particular, an emissions-based index largely reflecting end-of-pipe strategies and an inputs-based index reflecting integrated pollution prevention are distinguished. For the emissions-based index, a predominantly negative relationship between environmental and economic performance is found, whereas for the inputs-based index no significant link is found. This is consistent with the theoretical model, which predicts the possibility of different relationships. The results also show that for firms with pollution prevention-oriented corporate environmental strategies, the relationship between environmental and economic performance is more positive, thus making improvements in corporate sustainability more likely. Based on this last insight, managerial implications of this are discussed with regard to strategy choices, investment decisions and operations management. [Copyright &y& Elsevier]

This paper examines the impact of air pollution control policies targeting key polluting enterprises, highlighting a strategic shift towards precision pollution control that concentrates on high-emission, high-risk businesses. The paper explores the efficacy of these policies and their potential spatial spillover effects, utilizing panel data from 259 Chinese cities from 2013 to 2021. Employing the difference-in-differences (DID) model and spatial Durbin model, the study analyzes both the direct local effects and the broader spatial consequences of these regulatory measures on air quality. The findings indicate a significant reduction in air pollutant concentrations in urban areas, attributing this improvement to factors such as industrial restructuring, increased investment in science and technology, and economic growth. Spatial econometric analysis further reveals a substantial positive correlation in air quality among Chinese cities. However, estimates of the spillover effect indicate that while such policies successfully reduce pollution locally, they could unintentionally degrade air quality in adjacent areas. The study highlights the need for nuanced policy strategies to mitigate unintended spatial spillovers and enhance overall effectiveness. It recommends tailored policies that integrate environmental and socioeconomic objectives, national and regional coordination for consistent enforcement, technology-driven compliance strategies, and incentives for sustainable enterprise practices. • Assessed impact of policies on key polluters in 259 Chinese cities. • Employed DID and spatial Durbin models for in-depth policy impact analysis. • Found significant reduction in urban air pollution from policies. • Revealed spatial spillover effects, aligning with "pollution haven" hypothesis. • Proposed nuanced approaches for local and spatial policy impacts. [ABSTRACT FROM AUTHOR]

• Aerogel developed from CNF anchored with transition metal divalent ions enhanced by CNT. • Efficiently absorb ammonia, trimethylamine, hydrogen sulfide, and methyl mercaptan. • The hybrid aerogel showed steady performance in a wide range of temperatures from 0 °C to 40 °C. • The first aerogel type deodorizer material ever reported in air decontamination. • Aerogel is eco-friendly and economic, suitable for domestic and industrial uses. A novel deodorizer that is capable of selectively eliminating the odorous chemicals, such as ammonia, trimethylamine, hydrogen sulfide and methyl mercaptan, is described. The deodorizer is a nanostructured aerogel by nature, consisting of 2,2-6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidized cellulose nanofibrils (CNF), transition metal divalent cations (M2+), and multi-walled carbon nanotubes (CNT) as the constitutive elements. CNF are firstly mixed with M2+ (M2+, in this paper, typifies Ni2+, Co2+ and Cu2+) to form CNF-M2+ complexes, monodispersed CNT is then mixed to prepare CNT/CNF-M2+ waterborne slurries; CNT/CNF-M2+ hybridized aerogels are finally obtained via freezing-drying of the CNT/CNF-M2+ waterborne slurries. The CNT/CNF-M2+ aerogels are a foam-like structure consisting of CNF and CNT as backbones and M2+ as linkers. The aerogels show higher capabilities (in comparison with activated carbon) for selectively adsorbing ammonia, trimethylamine, hydrogen sulfide and methyl mercaptan. Computing simulations suggest a theoretical conclusion that the odorous chemicals are absorbed in a preferring manner of bimolecular absorptions via the M2+ moieties. The CNT/CNF-M2+ hybridized aerogels are lightweight, eco-friendly, and easy to produce in industrial scales. Our new finding, as is described in this paper, demonstrates potential applications of the TEMPO-oxidized CNF to the field of deodorizations. [ABSTRACT FROM AUTHOR]

This paper employs systematic literature network analysis, including a literature review and bibliometric network analysis, to explore the COVID-19 and air pollution literature. A total of 1208 relevant documents from the Scopus database were analyzed using VOSviewer and SciMAT to examine author, keyword, and country interconnections. The paper addresses three research questions: (1) the latest studies on COVID-19 and air pollution, (2) influential authors, documents, and sources in the field, and (3) the study's findings as a roadmap for future research. Visual representations of author and country networks highlight influential entities based on citation rate. Keyword cluster analysis via VOSviewer summarizes connections based on link strength. The strategic diagram generated by SciMAT offers insights into future research directions in specific thematic keywords: (1) air pollution, (2) nitrogen dioxide, (3) epidemiology, and (4) atmospheric aerosol. This analysis enhances understanding of COVID-19 and air pollution and guides future research endeavors. • Conducts a systematic review of the literature on COVID-19 and air pollution. • Utilizes bibliometric network analysis on a selection of documents. • Collects 1208 Scopus database documents from 2020 to 2023. • Identifies themes or keywords for further study on COVID-19 and air pollution. [ABSTRACT FROM AUTHOR]

Air pollution is an inevitable negative environmental externality in the process of industrialization and urbanization in countries around the world. Land misallocation in the process of urbanization and industrialization and the resulting urban sprawl exacerbates the above problems. As a major country in the stage of rapid urbanization and industrialization, China also faces severe air pollution problems. Therefore, this paper will systematically study the impact of land misallocation and urban sprawl on air pollution. We first set up the utility maximization problem of each economic entity in the process of land misallocation in the mathematical model, and form a general equilibrium theory model to analyze the relationship between land misallocation, urban sprawl and air pollution, and then put forward the research hypotheses. We further use the big data of land market transactions of China Land Market Network and LandScan global population data to build the land misallocation index and urban sprawl index respectively, and use an extended mediating effect model to empirically test the hypotheses. The results suggest that the effect of land misallocation on air pollution is significantly positive, and the impact of land misallocation on urban sprawl largely depends on the return rate of land relative to labor (RRIRL). When the RRIRL is relatively low, the impact is weak, or even has a negative effect to some extent. At this time, low transportation costs are conducive to reducing air pollution. On the contrary, when the RRIRL is relatively high, land misallocation has a significant role in promoting urban sprawl. At this time, high traffic costs are conducive to reducing air pollution. In addition, we also find that there is an inverted U-shaped relationship between urban sprawl and air pollution. When the urban sprawl level is low, its impact on air pollution increases with the increase of its level, but once it exceeds a specific node, the impact will decrease. Based on the characteristics of the relationship among land misallocation, urban sprawl and air pollution under different RRIRL, we summarize four environmentally friendly urban development paths. We bring the land bid rent theory and environmental pollution research into a same theoretical model framework, which provides a modeling idea and research perspective with a micro-foundation for subsequent research on land misallocation. We integrate the research conclusions of existing literature and unify them under the research frameworks concerning different RRIRL in the empirical results, which greatly improving their applicability. The four urban development paths summarized in this paper will also provide inspiration for future researchers and policy makers. • We posit that urban sprawl is an important intermediate mechanism by which land misallocation affects air pollution. • We build a general equilibrium framework. • Land misallocation leads to an increase in air pollutant concentrations. • Land misallocation has a nonlinear impact on air pollution by restraining urban sprawl. [ABSTRACT FROM AUTHOR]

This paper offers empirical insights into the trade-off between local air pollution and traffic accidents by exploiting an annual studded tyre ban in Norway. Studded tyres offer superior traction on icy roads, but also contribute to harmful particulate matter pollution. The use of studs is therefore restricted to certain dates which, due to its dependence on Easter, can differ by over a month from one year to another. I exploit this variation in a type of regression discontinuity in time design. The findings suggest that the use of studded tyres does not decrease the number of traffic accidents. Moreover, there is no evidence that studded tyres increase particulate matter pollution. As studded tyres increases road wear and tear and appears to not contribute to road safety, this paper raises the question of whether North-American and European countries where studded tyres are widely used should revisit their traffic safety policies. [ABSTRACT FROM AUTHOR]

Exposure to environmental noise is associated with adverse health effects, but there is potential for confounding and interaction with air pollution, particularly where both exposures arise from the same source, such as transport. To review evidence on confounding and interaction of air pollution in relation to associations between environmental noise and cardiovascular outcomes. Papers were identified from similar reviews published in 2013 and 2015, from the systematic reviews supporting the WHO 2018 noise guidelines, and from a literature search covering the period 2016–2022 using Medline and PubMed databases. Additional papers were identified from colleagues. Study selection was according to PECO inclusion criteria. Studies were evaluated against the WHO checklist for risk of bias. 52 publications, 36 published after 2015, were identified that assessed associations between transportation noise and cardiovascular outcomes, that also considered potential confounding (49 studies) or interaction (23 studies) by air pollution. Most, but not all studies, suggested that the associations between traffic noise and cardiovascular outcomes are independent of air pollution. NO 2 or PM 2.5 were the most commonly included air pollutants and we observed no clear differences across air pollutants in terms of the potential confounding role. Most papers did not appear to suggest an interaction between noise and air pollution. Eight studies found the largest noise effect estimates occurring within the higher noise and air pollution exposure categories, but were not often statistically significant. Whilst air pollution does not appear to confound associations of noise and cardiovascular health, more studies on potential interactions are needed. Current methods to assess quality of evidence are not optimal when evaluating evidence on confounding or interaction. • To our knowledge, the largest review to date to assess confounding by air pollution in studies of noise and CVD. • Also, the largest review to date to assess interactions of noise and air pollution in noise and CVD studies. • Results from the 52 noise studies reviewed showed little consistent evidence for a confounding role by air pollution. • Few studies investigated interactions, despite different proposed mechanisms for noise and air pollution on CVD risk. • There are no standard literature review approaches available to evaluate evidence on confounding and interaction. [ABSTRACT FROM AUTHOR]

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public. [Display omitted] • Examines diverse air pollution sources, including industry, transport, and natural events. • Comprehensively assesses global health risks, especially disease burden from particulate matter and chemicals. • Analyzes economic impacts of air pollution, including healthcare costs and productivity losses. • Discusses recent advances in interventions like clean energy and urban planning to address air pollution. [ABSTRACT FROM AUTHOR]

This paper studies how centralizing environmental administration affects air pollution in China. China launched a reform in 2016 to empower upper-level environmental protection bureaus to administer lower-level bureaus vertically through personnel control. Exploiting a stacked difference-in-differences strategy, we find that the reform significantly reduced air pollution. The effect was stronger in places where pollution was less likely to be affected by spillovers from other provinces, where local governments initially paid less attention to environmental protection, and where there was less economic importance. Further analysis shows that the reform reduced pollution by strengthening the pollution reduction incentives of local environmental officials, increasing the intensity of local environmental inspection, and promoting environmental compliance by polluting firms. [ABSTRACT FROM AUTHOR]

There is a knowledge gap on how ground-level particulate pollution affects labor productivity in emerging nations due to a lack of study, especially when it comes to outdoor work settings like couriers in the express delivery industry. Combining findings from two research projects, this paper examines the socioeconomic consequences of particulate matter and ground-level particulate pollution. Special panel dataset from China's express delivery companies are used, we study how particulate pollution affects courier productivity. The instrumental variable of our analysis was built by particulate pollution data from upwind towns. Moreover, a comparable rise in particulate levels during the 30 days caused a significant 23.7% decline in worker productivity. This draws attention to a neglected area of the economic effects of particulate pollution, especially in underdeveloped countries. Our results also highlight the wider health hazards connected to outdoor activities in high-pollution locations, drawing comparisons on outdoor exercisers and particulate matter concentrations. The critical need for coordinated policy attention addressing both ground-level Particulate and particle matters in developing nations is highlighted by the increased risk of lung function impairment among outdoor exercisers owing to excessive particulate matter concentrations. The interrelated risks that air pollution poses to public health and economic productivity are clarified by this Comprehensive viewpoint. • Examines the socioeconomic consequences of particulate matter and ground-level ozone pollution. • Using a special panel dataset from one of China's top express delivery companies. • Air pollution poses to public health and economic productivity are clarified. [ABSTRACT FROM AUTHOR]

Air pollution has been one of the biggest environmental challenges for cities in their pursuit of sustainability. The removal of air pollutants usually entails a cost that can negatively impact economic activities. Assessing urban environmental performance can offer valuable insights for cities to balance their economic production and environmental protection. This paper develops a novel approach to measuring environmental performance by using the Euclidean distance function. An appealing feature of this approach is its ability in endogenously allocating the optimization pathway to each emitter, thereby avoiding arbitrary estimation results and distorted managerial implications. We apply this approach to study the environmental performance of Chinese key environmental protection cities. We find the heterogeneity in performance estimates and endogenous optimization pathways. Prioritizing the reduction of a specific type of emissions while simultaneously increasing industrial output value seems to be the most appropriate objective for the majority of cities. Our study can serve as a basis for urban governments to pinpoint the underlying factors contributing to low urban environmental performance and establish diverse objectives for enhancing environmental performance. • Environmental performance index is constructed based on endogenous directions. • A Euclidean distance function is used to identify endogenous directions. • Optimization pathways and environmental performance show heterogeneity among cities. • Mitigating NO x emissions can improve environmental performance significantly. [ABSTRACT FROM AUTHOR]

Current urban and transport planning practices have significant negative health, environmental, social and economic impacts in most cities. New urban development models and policies are needed to reduce these negative impacts. The Superblock model is one such innovative urban model that can significantly reduce these negative impacts through reshaping public spaces into more diverse uses such as increase in green space, infrastructure supporting social contacts and physical activity, and through prioritization of active mobility and public transport, thereby reducing air pollution, noise and urban heat island effects. This paper reviews key aspects of the Superblock model, its implementation and initial evaluations in Barcelona and the potential international uptake of the model in Europe and globally, focusing on environmental, climate, lifestyle, liveability and health aspects. We used a narrative meta-review approach and PubMed and Google scholar databases were searched using specific terms. The implementation of the Super block model in Barcelona is slow, but with initial improvement in, for example, environmental, lifestyle, liveability and health indicators, although not so consistently. When applied on a large scale, the implementation of the Superblock model is not only likely to result in better environmental conditions, health and wellbeing, but can also contribute to the fight against the climate crisis. There is a need for further expansion of the program and further evaluation of its impacts and answers to related concerns, such as environmental equity and gentrification, traffic and related environmental exposure displacement. The implementation of the Superblock model gained a growing international reputation and variations of it are being planned or implemented in cities worldwide. Initial modelling exercises showed that it could be implemented in large parts of many cities. The Superblock model is an innovative urban model that addresses environmental, climate, liveability and health concerns in cities. Adapted versions of the Barcelona Superblock model are being implemented in cities around Europe and further implementation, monitoring and evaluation are encouraged. The Superblock model can be considered an important public health intervention that will reduce mortality and morbidity and generate cost savings for health and other sectors. [ABSTRACT FROM AUTHOR]

Most developed economies invest in public goods such as national defense, education, infrastructure, and the environment. Discount rates used to evaluate such projects should reflect the rate of return on the current mix of investment opportunities. Rates derived from the productivity of private capital neglect returns beyond the market boundary. The present paper derives discount rates using an augmented measure of national income inclusive of non-market goods. In an empirical application focusing on air pollution and climate damages in the United States economy, the paper reports that the difference between augmented and market discount rates averages 0.3 percentage points from 1999 to 2014. [ABSTRACT FROM AUTHOR]

Urban stressors pose a health risk, and individual-level assessments provide necessary and fine-grained insight into exposure. An ever-increasing amount of research literature on individual-level exposure to urban stressors using data collected with personal monitors, has called for an integrated assessment approach to identify trends, gaps and needs, and provide recommendations for future research. To this end, a scoping review of the respective literature was performed, as part of the H2020 URBANOME project. Moreover, three specific aims were identified: (i) determine current state of research, (ii) analyse literature according with a waterfall methodological framework and identify gaps and needs, and (iii) provide recommendations for more integrated, inclusive and robust approaches. Knowledge and gaps were extracted based on a systematic approach, e.g., data extraction questionnaires, as well as through the expertise of the researchers performing the review. The findings were assessed through a waterfall methodology of delineating projects into four phases. Studies described in the papers vary in their scope, with most assessing exposure in a single macro domain, though a trend of moving towards multi-domain assessment is evident. Simultaneous measurements of multiple stressors are not common, and papers predominantly assess exposure to air pollution. As urban environments become more diverse, stakeholders from different groups are included in the study designs. Most frequently (per the quadruple helix model), civil society/NGO groups are involved, followed by government and policymakers, while business or private sector stakeholders are less frequently represented. Participants in general function as data collectors and are rarely involved in other phases of the research. While more active involvement is not necessary, more collaborative approaches show higher engagement and motivation of participants to alter their lifestyles based on the research results. The identified trends, gaps and needs can aid future exposure research and provide recommendations on addressing different urban communities and stakeholders. [ABSTRACT FROM AUTHOR]

Ozone (O 3) pollution in the atmosphere is getting worse in many cities. In order to improve the accuracy of O 3 prediction and obtain the spatial distribution of O 3 concentration over a continuous period of time, this paper proposes a VAR-XGBoost model based on Vector autoregression (VAR), Kriging method and XGBoost (Extreme Gradient Boosting). China is used as an example and its spatial distribution of O 3 is simulated. In this paper, the O 3 concentration data of the monitoring sites in China are obtained, and then a spatial prediction method of O 3 mass concentration based on the VAR-XGBoost model is established, and finnally its influencing factors are analyzed. This paper concludes that O 3 features the highest correlation with PM 2.5 and the lowest correlation with SO 2. Among the measurement factors, wind speed and temperature are the most important factors affecting O 3 pollution, which are positively correlated to O 3 pollution. In addition, precipitation is negatively correlated with 8-hour ozone concentration. In this paper, the performance of the VAR-XGBoost model is evaluated based on the ten-fold cross-validation method of sample, site and time, and a comparison with the results of XGBoost, CatBoost (categorical boosting), ExtraTrees, GBDT (gradient boosting decision tree), AdaBoost (adaptive boosting), RF (random forest), Decision tree, and LightGBM (light gradient boosting machine) models is conducted. The result shows that the prediction accuracy of the VAR-XGBoost model is better than other models. The seasonal and annual average R2 reaches 0.94 (spring), 0.93 (summer), 0.92 (autumn), 0.93 (winter), and 0.95 (average from 2016 to 2021). The data show that the applicability of the VAR-XGBoost model in simulating the spatial distribution of O 3 concentrations in China performs well. The spatial distribution of O 3 concentrations in the Chinese region shows an obvious feature of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The mean concentration is clearly low in winter and high in summer within a season. The results of this study can provide a scientific basis for the prevention and control of regional O 3 pollution in China, and can also provide new ideas for the acquisition of data on the spatial distribution of O 3 concentrations within cities. • VAR model estimate the dynamic relationships of all endogenous variables. • VAR-tree model estimate the spatial and temporal distribution of O 3. • The eight-tree model improve the accuracy of O 3 change prediction. • VAR-XGBoost method solve the problem of spatial heterogeneity of the study object. [ABSTRACT FROM AUTHOR]

Background An increasing number of studies have suggested adverse effects of air pollution on mental health. Given the potentially negative impacts of ozone exposure on the immune and nervous system driven from animal experiments, ozone might also affect mental health. However, no systematic synthesis of the relevant literature has been conducted yet. This paper reviews the studies that assessed the link between ozone exposure and mental health thus far. Methods We followed the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA). PubMed, Web of Science, and EMBASE were systematically searched for epidemiological studies on ambient ozone exposure and mental or behavioral disorders according to the International Classification of Disease. The period was from January 1st, 1960 to December 14st, 2017. We evaluated the risk of bias by the Office of Health Assessment and Translation (OHAT) Approach and Navigation Guide for each included study. Results The keyword search yielded 567 results. 31 papers met the selection criteria and were included in the review. We found only inconclusive evidence that ozone affects autism spectrum disorders, impairment of cognitive functions and dementia, depression, and suicide. The large heterogeneity of study designs, outcome definitions and study quality in general prevented us from conducting meta-analyses. Conclusions Current evidence for an association between ambient ozone exposure and mental health outcomes is inconclusive and further high quality studies are needed to assess any potential links given the strong biologic plausibility. [ABSTRACT FROM AUTHOR]

Background Particulate matter (PM) has been recognized as one of the key risk factors of lung cancer. However, spatial and temporal patterns of this association remain unclear. Spatiotemporal analyses incorporate the spatial and temporal structure of the data within random effects models, generating more accurate evaluations of PM-lung cancer associations at a scale that can better inform lung cancer prevention programs. Methods We conducted a critical review of spatial and temporal analyses of PM and lung cancer. The databases of PubMed, Web of Science and Scopus were searched for potential articles published until September 30, 2017. We included studies that applied spatial and temporal analyses to evaluate the associations of PM 2.5 (inhalable particles with diameters that are 2.5 µm and smaller) and PM 10 (inhalable particles with diameters that are 10 µm and smaller) with lung cancer. Results We identified 17 articles eligible for the review. Of these, 11 focused on PM 2.5 , five on PM 10 , and one on both. These studies suggested a significant positive association between PM 2.5 exposure and the risk of lung cancer. Relative risks of lung cancer mortality ranged from 1.08 (95% confidence interval (CI): 1.07–1.09) to 1.60 (95%CI: 1.09–2.33) for 10 µg/m 3 increase in PM 2.5 exposure. The association between PM 10 and lung cancer had been less well researched and the results were not consistent. In terms of the analysis methods, 16 papers undertook spatial analysis and one paper employed temporal analysis. No paper included spatial and temporal analyses simultaneously and considered spatiotemporal uncertainty into model predictions. Among the 16 papers with spatial analyses, thirteen studies presented maps, while only five and 11 studies utilized spatial exploration and modeling methods, respectively. Conclusions Advanced spatial and temporal epidemiological methods were seldom applied to PM-lung cancer associations. Further research is urgently needed to develop and employ robust and comprehensive spatiotemporal analysis methods for the evaluation of PM-lung cancer associations and the support of lung cancer prevention strategies. [ABSTRACT FROM AUTHOR]

There is an increasing interest in considering the effects of air pollution on ecosystem services supply in order to enhance cost-benefit analyses of air pollution policies. This paper presents a generic, conceptual approach that can be used to link atmospheric deposition of air pollutants to ecosystem services supply and societal benefits. The approach is applied in a case study in the Telemark county of Norway. First, we examine the potential effects of four European air quality policy scenarios on N deposition in the ecosystems of this county. Second, we analyse the subsequent impacts on the supply of three ecosystem services: carbon sequestration, timber production and biodiversity. Changes in the supply of the first two services are analysed in both physical and monetary units, biodiversity effects are only analysed in physical terms. The scenarios derive from work conducted in the context of the European National Emissions Ceilings Directive. In the 2010 base case the benefits of carbon sequestration are estimated at 13 million euro per year and the value of timber harvesting at 2.9 million euro per year. Under the examined policy scenarios aiming to reduce nitrogen emissions the societal benefits resulting from these two ecosystem services in Telemark are found to be reduced; the scenarios have little effect on terrestrial biodiversity. Such results cannot be scaled up, individual ecosystem services respond differently to changes in air pollution depending upon type of pollutant, type of ecosystem, type of service, and the magnitude of change. The paper further presents an analysis of the uncertainties that need to be considered in linking air pollution and ecosystem services including those in deposition rates, ecosystem responses, human responses and in the values of ecosystem services. Our conceptual approach is also useful for larger scale analysis of air pollution effects on ecosystem services, for example at national or potentially European scale. [ABSTRACT FROM AUTHOR]

Plastic waste trade has grown considerably in the last decades and has led to severe environmental problems in recipient countries. Being the largest recipient, China has permanently banned the imports of plastic waste since 2018. This paper examines the causal effect of plastic waste imports on air pollution by exploiting China's experience of importing plastic waste and the recent import ban. By combining data on plastic waste imports with PM 2.5 data at the city level for the years 2000–2011, we find that plastic waste imports increased PM 2.5 density significantly in recipient cities. To evaluate the impact of the import ban on air quality, we employ daily data on air pollution between 2015 and 2020. Our difference-in-differences results show that affected cities, relative to other cities, experienced considerable improvement in air quality following the ban. Further analysis reveals that increased incineration of non-recycled waste is the main channel. These findings provide insights into the costs of importing plastic waste and the potential environmental gains from banning such imports in other countries. • We assess the environmental impact of plastic waste trade in the context of China. • Chinese cities that imported more plastic waste experienced an increase in PM2.5 density between 2000 and 2011. • The primary factor contributing to worsened air quality was the increased incineration of non-recycled waste. • China's import ban in 2018 effectively improved air quality. • The findings highlight the importance of designing effective policies to regulate international trade in waste and domestic waste management. [ABSTRACT FROM AUTHOR]

With the ongoing process of industrialization, the issue of declining air quality is increasingly becoming a critical concern. Accurate prediction of the Air Quality Index (AQI), considered as an all-inclusive measure representing the extent of pollutants present in the atmosphere, is of paramount importance. This study introduces a novel methodology that combines stacking ensemble and error correction to improve AQI prediction. Additionally, the reptile search algorithm (RSA) is employed for optimizing model parameters. In this study, four distinct regional AQI data containing a collection of 34864 data samples are collected. Initially, we perform cross-validation on ten commonly used single models to obtain prediction results. Then, based on evaluation indices, five models are selected for ensemble. The results of the study show that the model proposed in this paper achieves an improvement of around 10% in terms of accuracy when compared to the conventional model. Thus, the model introduced in this study offers a more scientifically grounded approach in tackling air pollution. • A new AQI prediction model for Stacking ensemble learning is proposed. • The ensemble model is combined with the error correction model. • Using RSA algorithm to optimize the parameters of the Stacking ensemble models. • Based on the indicator, 5 models are selected for ensemble to obtain better prediction results. [ABSTRACT FROM AUTHOR]

Abstract: I review the rationale for the Environmental Protection Agency’s 1996 fine particle standard, which was based almost entirely on the epidemiological data with neither support from Toxicology nor understanding of mechanism. While many epidemiological papers available in 1996 reported associations between ambient particles and adverse effects on human health, many others did not and the evidence fell far short of supporting a causal association between particle mass concentration and human health. The literature appearing after 1996 further complicates the picture. The large studies that have appeared after 1996, such as National Mortality Morbidity and Air Pollution Study, and the reanalyses of the American Cancer Society II study, report risks that are substantially smaller than the risks reported in the 1996 Criteria Document and Staff Paper. Moreover, concerns about confounding by weather, temporal trends and co-pollutants remain unresolved. Other issues having to do with model choice have resurfaced as a result of reanalyses of critical data to address a glitch in a widely used software package for time-series epidemiology studies of air pollution. Finally, contemporary examples show that the results of observational epidemiology studies can be seriously biased, particularly when estimated risks are small, as is the case with studies of air pollution. The Agency has largely ignored these issues. I conclude that a particle mass standard is not defensible on the basis of a causal association between ambient particle mass and adverse effects on human health. Such a standard may be justifiable on the basis of the precautionary principle, however. The Agency could argue that the Science raises concerns about current levels of air pollution, and that reduction of ambient fine particulate matter mass, if it could be achieved without an increase in the level of the ultrafines, could have positive effects on human health. If the Agency justifies a particulate matter mass standard on these grounds then the debate over the form and level of the standard will, for all practical purposes, belong strictly in the Policy arena. [Copyright &y& Elsevier]

This paper examines invasive species management when invasive species impact health outcomes indirectly through changes to environmental quality. For example, the emerald ash borer (EAB) has destroyed millions of ash trees throughout North America and has the potential to impact rates of cardiorespiratory mortality and morbidity through ash trees’ ability to capture airborne pollutants. Optimal management inclusive of indirect health externalities may be different than status quo plans because the links between nature and health are complex, dynamic, and spatially heterogeneous. We produce a novel dynamic bioeconomic-health model to determine optimal EAB management in the face of such health effects. Our results show that including health increases net benefits of management substantially and that a “one size fits all” management approach is suboptimal given forest cover and demographic spatial heterogeneity. Net benefits to society are 873% higher and air pollution related mortality incidence is 82% lower when health externalities are included in management profiles using insecticide treatments and non-ash tree preemptive plantings without removal. Additionally, constrained managers optimally substitute toward preemptive tree plantings and away from insecticide use in the presence of indirect health externalities as a way to minimize disruptions to air quality. This paper has policy implications for the optimal management of environmental amenities. [ABSTRACT FROM AUTHOR]

The prediction of air pollution plays an important role in reducing the emission of air pollutants and guiding people to carry out early warning and control, so it attracts many scholars to conduct modeling and research on it. However, most of the current researches fail to quantify the uncertainty in prediction and only use traditional fuzzy information granulation to process data, resulting in the loss of much detail information. Therefore, this paper proposes a hybrid model based on decomposition and granular fuzzy information to solve these problems. The trend item and the Granulation fluctuation item are respectively predicted and the results are combined to obtain the change trend and fluctuation range of the sequence. This paper selects PM 2.5 concentrations of 3 cities. The experimental results show that the evaluation index of the prediction model is significantly lower than other benchmark models, and a variety of statistical methods are used to further verify the effectiveness of the prediction model. • Quantify the uncertainty of data to obtain a more complete physical meaning. • Fuzzy theory strategies more scientifically measure data characteristics. • The combination of trend term and fluctuation term process data more rationally. • The modified optimization algorithm expands the application scope of the model. [ABSTRACT FROM AUTHOR]

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM 2.5), nitrogen dioxide (NO 2), and sulfur dioxide (SO 2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e. , pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM 2.5 , NO 2 , and SO 2 were 1.016 (95% confidence interval, CI: 1.006–1.026), 1.063 (1.045–1.090), and 1.048 (1.031–1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities. • Industrial PM 2.5 , NO 2 , and SO 2 levels were estimated by a chemical transport model. • Childhood asthma onset rates decreased with decreased industrial pollutants levels. • The strongest association by industrial sector was with PM 2.5 from metal smelters. [ABSTRACT FROM AUTHOR]

Information about a company's environmental, social and governance (ESG) performance has become increasingly important in the decision-making process of financial institutions. The financial implications of environmental challenges (e.g. water stress), negative social impacts (e.g. health impacts in local communities) or poor corporate governance (e.g. breaching legislation) all continue to increase. Accordingly, there is a need for financial institutions to incorporate information on ESG risks, opportunities and impacts in decisions that relate to risk management, investments, credit, strategy, and reporting. ESG information is typically disseminated through ESG ratings, which combine the three constituents into a single rating, or ascribe them separate scores. The compilation of ESG ratings and the identification of appropriate data sources is an inherently complex process; as such, there is no single standard for data collection or reporting. This has led to a divergence in the underlying data sources used by different rating providers, as well as in the determination of factors that are deemed worthy of measurement in the first place. For example, when assessing a company's environmental impact, one rating provider may rely on company-provided data, while another may incorporate independent third-party assessments. Unfortunately, there is currently no clear mechanism for effectively resolving such disagreements to establish a standardised approach to ESG rating assessments. However, geospatial data and analyses offer several key advantages for ESG assessments, including consistency, the potential for enhanced accuracy, and the ability to identify and assess environmental impacts at a detailed physical asset level, in addition to evaluating the broader spatial context. By incorporating geospatial information (obtained through manually processing remotely sensed data, or by using existing products) rating methodologies can be improved, and disparities can be addressed more effectively. This would enable a more comprehensive understanding of the environmental considerations of ESG assessments, promoting a more informed and precise decision-making process. Within this context, a few institutions (e.g. the University of Oxford, the WWF, and a few others) are pioneering thought leadership around spatial finance, including the assessment of ESG issues utilising geospatial intelligence, but there are no consistent frameworks for incorporating geospatial data into ESG ratings and analysis. This paper explores the opportunity for such a geospatial environmental scoring framework, defining a variety of methods in which open data with broad geographic coverage could be incorporated into ESG analysis, generalisable to a range of assets and sectors. The proposed framework is organised into two categories: localised effects, which directly impact the immediate vicinity of an asset, and delocalised effects, which contribute to global climate change and atmospheric pollution. Sub-scores are defined within these categories, which capture both the localised effects on land use, biodiversity, soils, and hydrology, and the global impacts resulting from atmospheric emissions. The approaches for handling geospatial data to generate both these sub-scores and the final E-score are presented, including a test case, and the complete methodology is made available in open repositories. • First geospatial framework for corporates and investors to assess environmental impacts of a study area. • Transparent and accessible framework, making use of open access data and tools. • Practical description of the framework with an industrial test case. [ABSTRACT FROM AUTHOR]

Due to the acceleration of urbanization and industrialization, air pollutants has been increasing, posing a major threat to human health and the ecological environment. During the past period of rapid growth, with the booming development of real estate, the air pollutants brought about by the construction of housing buildings have become more and more serious, especially sulfur dioxide, nitrogen dioxide, and dust, which cast a great threat to human life and seriously jeopardize human health. Compared with the traditional construction of houses, prefabricated buildings construction procedures are reduced, to some extent, can reduce air pollutants. Therefore, this paper takes an empirical perspective to include prefabricated buildings and air pollutants into the same research framework and analyze the inherent logical relationship. We found that prefabricated buildings have a substantial role in mitigating urban air pollution. This conclusion remained solid after various robustness tests including constructing instrumental variables. The effect is observed to be greater in cities above the non-sub-provincial level and those positioned within central regions. The conclusions explore new areas for the green and sustainable development of the construction industry, which in the future will have to fill the requirements of green development in order to survive. Else, perspectives from the construction industry, it provides a realistic solution to economic development and ecological protection, helping to alleviate or resolve the conflict between development and survival. • The air pollution abatement effect of prefabricated buildings is investigated. • The results are robust after controlling the endogenous issue. • The heterogeneity is supported for different samples and air pollutants. [ABSTRACT FROM AUTHOR]

The "Air Pollution Prevention and Control Action Plan" is regarded as China's war against air pollution, which is also noted as China's Clean Air Action (CAA policy). Although abundant studies have focused on the impact of CAA policy on social activities, such as air quality, pollutants emission, and economic output, limited studies explore its impact on enterprise competitiveness from a more micro perspective. Improving total factor productivity (TFP) is essential to achieve economic efficiency and sustainable development. Identifying the impact and influence mechanism of the CAA policy on TFP is important for China to realize the coordinated development of environment and economy and promote high-quality economic development. Based on the difference-in-differences model, this paper evaluates the impact of the CAA policy on enterprise productivity. The empirical analysis shows that there is a significantly negative effect from the CAA policy to the TFP of polluting enterprises, indicating that the CAA policy has decreased enterprise productivity. Our finding is robust, after conducting several robustness tests, such as replacing different dependent indicators, adopting different policy measurement variables, considering other policy interventions and using propensity score matching. Besides, this paper also discusses the possible heterogeneities. This paper adds additional empirical evidence to understand the relationship between air pollution control and enterprise competitiveness. The conclusions of this paper have important practical significance for China to further promote air pollution control and perfect the environmental control system. • We analyze the impact of China's Clean Air Action on enterprise competitiveness. • There is a negative effect from the Clean Air Action policy to enterprise total factor productivity. • Possible heterogeneities have been discussed from several aspects. • This paper adds additional empirical evidence to understand China's Clean Air Action. [ABSTRACT FROM AUTHOR]

It is an urgent challenge to coordinate industrialization and air quality in China since the rapid industrialization over the past forty years caused serious air pollution. In this paper, we measured the industrialization levels of the 31 regions in China from 2003 to 2017 as a sum of per capita gross domestic product (GDP), urban population (% of total), tertiary vs. secondary industry output ratio, non-agricultural output value (% of GDP), non-agricultural employment proportion, and the proportion of added value of manufacturing in the total added value of commodity. We measured air quality as a sum of annual SO 2 , NO 2 and PM 10 concentrations. In general, the industrialization level continuously increased while air pollution was reduced in spite of some fluctuations in China. Our results show air pollution presented N-shape relations with the industrialization level in China in the periods from 2003 to 2008, 2009 to 2013, and 2014 to 2017. Air pollution increases as the industrialization occurs but later deceases as the industrialization level improves. However, air pollution rebounds as the industrialization moves further but this rebound became weaker in recent years. With the association rule mining, we identified ranges of the six indicators for industrialization associated with good and poor air quality, respectively. Using industrialization indicators associated with poor air quality as references, we pointed out differentiated strategies for regions to coordinate the industrialization and air quality protection. • We analyzed industrialization progress & air quality from 2003 to 2017 in China. • Air pollution in China exhibits N-shape relations with industrialization level. • We mined association rules between industrialization indicators and air quality. • Summary of industrialization indicator ranges associated with good/poor air quality. • Differential strategy by region to coordinate development & air quality protection. [ABSTRACT FROM AUTHOR]

Due to variations in economic scale, economic structure, and technological advancement across different Chinese provinces and cities, the cost of air pollution reduction differs significantly. Therefore, the total reduction cost can be decreased by capitalizing on these regional discrepancies in reduction cost to carry out cooperative emission reduction. In this paper, taking NO x reduction in North China as an example, a regional cooperative reduction game (CRG) model was constructed to minimize the total cost of emission reduction while achieving future emission reduction targets. The fair allocation of benefits from cooperation plays a crucial role in motivating regions to participate into the cooperation. A comprehensive mechanism of benefits allocation was proposed to achieve fair transferred compensation. The mechanism combines the consumption responsibility principle based on input-output theory and the Shapley value method based on game theory. Compared to the cost before the optimized collaboration, the CRG model will save 20.36% and 13.71% of the total reduction cost in North China, respectively, under the target of 17.68% NO x reduction by 2025 and 66.44% NO x reduction by 2035 relative to 2020. This method can be employed in other regions to achieve targets for air pollution reduction at minimum cost, and to motivate inter-regional cooperation with this practical and fair way of transferred compensation. [Display omitted] • A regional cooperative reduction game model for air pollution was constructed. • We projected NO x reduction cost savings in North China under two future scenarios. • An integrated approach was used to allocate cooperative benefits with fairness. • The model helps achieve air pollution reduction targets at the minimum cost. [ABSTRACT FROM AUTHOR]

Exposure to particulate matter (PM) has been associated with a range of health impacts, including neurological abnormalities that affect neurodevelopment, neuroplasticity, and behavior. Recently, there has been growing interest in investigating the possible relationship between PM exposure and the onset and progression of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis. However, the precise mechanism by which PM affects neurodegeneration is still unclear, even though several epidemiological and animal model studies have provided mechanistic insights. This article presents a review of the current research on the neurotoxicity of PM and its impact on neurodegenerative diseases. This review summarizes findings from epidemiological and animal model studies collected through searches in Google Scholar, PubMed, Web of Science, and Scopus. This review paper also discusses the reported effects of PM exposure on the central nervous system and highlights research gaps and future directions. The information presented in this review may inform public health policies aimed at reducing PM exposure and may contribute to the development of new treatments for neurodegenerative diseases. Further mechanistic and therapeutic research will be needed to fully understand the relationship between PM exposure and neurodegenerative diseases. • PM, a constituent of air pollution, has negative health impacts on individuals. • Interest in studying the neurodegenerative exacerbations of PM exposure has grown. • This review provides an up-to-date understanding of PM-induced neurodegeneration. • This information can help develop future therapies for PM-induced neurotoxicity. [ABSTRACT FROM AUTHOR]

Severe air quality hurts human capital and threatens innovative outcomes. Using unique data containing 12.8 million patent applications in China, this paper examines the causal effect of particulate matter with a diameter of 2.5 μm or less (PM 2.5) on patent innovation. We estimate a two-stage least square model with thermal inversion as an instrumental variable. Our findings show that a one μg/m3 increase in the annual average PM 2.5 concentration leads to a 1.3% decrease in the number of patents. Annual fluctuations in PM 2.5 concentration levels across cities caused the total number of patents to decrease by 1.1% during the 2006–2010 period. From 2011 to 2015, the improvement in air quality increased the number by about 2.0%. It demonstrates another innovation co-benefit of improving air quality due to the tightened regulation. [ABSTRACT FROM AUTHOR]

Following market liberalization, the vehicle population in China has increased dramatically over the past few decades. This paper examines the causal impact of the opening of a heavily used high speed rail line connecting two megacities in China in 2015, Chengdu and Chongqing, on air pollution. We use high-frequency and high spatial resolution data to track pollution along major highways linking the two cities. Our approach involves the use of a novel augmented regression discontinuity in time approach that incorporates machine learning to inform our specification choice in the first stage. Our estimates show that CO is reduced by 6.4% and PM 2.5 by 7.1% along the main affected highway. These findings are supported using a difference-in-differences approach. [ABSTRACT FROM AUTHOR]

Air pollution is one of the top five causes of death in the world and has become a research hotspot. In the past, the health effects of particulate matter (PM), the main component of air pollutants, were mainly focused on the respiratory and cardiovascular systems. However, in recent years, the intestinal damage caused by PM and its relationship with gut microbiome (GM) homeostasis, thereby affecting the composition and function of GM and bringing disease burden to the host lung through different mechanisms, have attracted more and more attention. Therefore, this paper reviews the latest research progress in the effect of PM on GM-induced lung damage and its possible interaction pathways and explores the potential immune inflammatory mechanism with the gut-lung axis as the hub in order to understand the current research situation and existing problems, and to provide new ideas for further research on the relationship between PM pollution, GM, and lung damage. [ABSTRACT FROM AUTHOR]

Air pollution is the existence of atmospheric chemicals damaging the health of human beings and other living organisms or damaging the environment or resources. Rarely any common contaminants are smog, nicotine, mold, yeast, biogas, or carbon dioxide. The paper will primarily observe, visualize and anticipate pollution levels. In particular, three algorithms of Artificial Intelligence were used to create good forecasting models and a predictive AQI model for 4 distinct gases: carbon dioxide, sulphur dioxide, nitrogen dioxide, and atmospheric particulate matter. Thus, in this paper, the Air Qualification Index is developed utilizing Linear Regression, Support Vector Regression, and the Gradient Boosted Decision Tree GBDT Ensembles model over the next 5 h and analyzes air qualities using various sensors. The hypothesized artificial intelligence models are evaluated to the Root Mean Squares Error, Mean Squared Error and Mean absolute error, depending upon the performance measurements and a lower error value model is chosen. Based on the algorithm of the Artificial Intelligent System, the level of 5 air pollutants like CO2, SO2, NO2, PM 2.5 and PM10 can be predicted immediately by integrating the observations with errors. It may be used to detect air quality from distance in large cities and can assist lower the degree of environmental pollution. • This research will primarily observe, visualize and anticipate pollution levels. • Air Qualification Index is developed utilizing Linear Regression, Support Vector Regression, and GBDT Ensembles model. • The proposed model is chosen to predict the 5 h air quality index. [ABSTRACT FROM AUTHOR]

Since the rise of the Covid-19 pandemic, several researchers stated the possibility of a positive relationship between Covid-19 spread and climatic parameters. An ecological study in 12 Iranian cities using the report of daily deaths from Covid-19 (March to August 2020) and validated data on air pollutants, considering average concentrations in each city in the last year used to analyze the association between chronic exposure to air pollutants and the death rate from Covid-19 in Iran. Poisson regression models were used, with generalized additive models and adjustment variables. A significant increase of 2.7% (IC(95%) 2.6–4.4) was found in the mortality rate due to Covid-19 due to an increase of 1 μg/m3 of NO 2. The results suggest an association between Covid-19 mortality and NO 2 exposure. As a risk approximation associated with air pollution, more precise analysis is done. The results also show a good consistency with studies from other regions; this paper's results can be useful for the public health policymakers and decision-making to control the Covid-19 spread. • A reliable Covid-19 data from Iran was used in this paper. • 12 cities were considered from all of the minor climate conditions of the country to minimize the climate bias. • Correlation of the air pollution and the other most effective diseases on the Covid-19 is also studied. • Nox was the most effective particle and had a significant impact on the Covid-19 mortality rate. • For a 1% increase in the NO 2 concentration, there will be a 2.7% increase in the Covid-19 mortality rate. [ABSTRACT FROM AUTHOR]

This paper argues that both the physical material of the environment as well as the social imagination of the environment are important actors in the production of urban space. Using a case study of the smelter city of Anaconda, Montana in the twentieth century, this paper shows how toxic smelter smoke and the complex social imagination of that smoke shape the contours of urban space. The production of smelter smoke for nearly a century in Anaconda persists today and presents a complex set of opportunities and challenges for the continuing development of the urban landscape. By taking a smoke-specific approach to the relationship between cities and industrial air pollution, this paper contends that material nature matters in the everyday urban experiences of life in this postindustrial city. [ABSTRACT FROM AUTHOR]

Indirect adjustment via partitioned regression is a promising technique to control for unmeasured confounding in large epidemiological studies. The method uses a representative ancillary dataset to estimate the association between variables missing in a primary dataset with the complete set of variables of the ancillary dataset to produce an adjusted risk estimate for the variable in question. The objective of this paper is threefold: 1) evaluate the method for non-linear survival models, 2) formalize an empirical process to evaluate the suitability of the required ancillary matching dataset, and 3) test modifications to the method to incorporate time-varying exposure data, and proportional weighting of datasets. We used the association between fine particle air pollution (PM 2.5) with mortality in the 2001 Canadian Census Health and Environment Cohort (CanCHEC, N = 2.4 million, 10-years follow-up) as our primary dataset, and the 2001 cycle of the Canadian Community Health Survey (CCHS, N = 80,630) as the ancillary matching dataset that contained confounding risk factor information not available in CanCHEC (e.g., smoking). The main evaluation process used a gold-standard approach wherein two variables (education and income) available in both datasets were excluded, indirectly adjusted for, and compared to true models with education and income included to assess the amount of bias correction. An internal validation for objective 1 used only CanCHEC data, whereas an external validation for objective 2 replaced CanCHEC with the CCHS. The two proposed modifications were applied as part of the validation tests, as well as in a final indirect adjustment of four missing risk factor variables (smoking, alcohol use, diet, and exercise) in which adjustment direction and magnitude was compared to models using an equivalent longitudinal cohort with direct adjustment for the same variables. At baseline (2001) both cohorts had very similar PM 2.5 distributions across population characteristics, although levels for CCHS participants were consistently 1.8–2.0 μg/m3 lower. Applying sample-weighting largely corrected for this discrepancy. The internal validation tests showed minimal downward bias in PM 2.5 mortality hazard ratios of 0.4–0.6% using a static exposure, and 1.7–3% when a time-varying exposure was used. The external validation of the CCHS as the ancillary dataset showed slight upward bias of −0.7 to −1.1% and downward bias of 1.3–2.3% using the static and time-varying approaches respectively. The CCHS was found to be fairly well representative of CanCHEC and its use in Canada for indirect adjustment is warranted. Indirect adjustment methods can be used with survival models to correct hazard ratio point estimates and standard errors in models missing key covariates when a representative matching dataset is available. The results of this formal evaluation should encourage other cohorts to assess the suitability of ancillary datasets for the application of the indirect adjustment methodology to address potential residual confounding. • Indirect adjustment can be reliably applied to survival models. • Direction and magnitude of confounding is data and model specific. • Formal evaluation of ancillary matching data is critical to assess adjustment bias. [ABSTRACT FROM AUTHOR]

Major cities in China and many other fast-growing economies are expanding their subway systems in order to address worsening air pollution and traffic congestion. This paper quantifies the impact of subway expansion on air quality by leveraging fine-scale air quality data and the rapid build-out of 14 new subway lines and 252 stations in Beijing from 2008 to 2016. Our main empirical framework examines how the density of the subway network affects air quality across different locations in the city during this period. To address the potential endogenous location of subway stations, we construct an instrument based on historical subway planning, long before air pollution and traffic congestion were of concern. Our analysis shows that an increase in subway density by one standard deviation improves air quality by two percent and the result is robust to a variety of alternative specifications including the distance-based difference-in-differences method. The total discounted health benefit during a 20-year period from reduced mortality and morbidity as a result of 14 new subway lines amounts to $1.0–3.1 billion, or only 1.4–4.4 percent of the total construction and operating cost. [ABSTRACT FROM AUTHOR]

ADHD is a common neurodevelopmental disorder highly attributed to genetics, but the combination of other social and environmental determinants, as well as potential gene-environment interactions, can also be responsible. This paper aims to review relevant literature published up to April 2018 for determining whether air pollution caused by ambient gaseous (NO 2 , SO 2 , PCDD/Fs, Benzene) and particulate matters (PM 10 , PM 2.5 , PM 7 , PAH, BC/EC) as an environmental risk factor is associated with increased risk of ADHD in children. Relevant literature was identified through electronic searches of PubMed, Embase, Web of Science, Scopus database and gray literature. A total of 872 articles were initially identified 28 of which meeting the defined inclusion criteria were included. The methodological quality of the included articles was evaluated using the modified Critical Appraisal Skills Programs (CASP) and confounding variables, exposure and outcome measurement were assessed. The results of this systematic review revealed that there is more evidence on the detrimental effects of EC, BC, and PM on ADHD compared to PAH. Among gaseous air pollutants, association was found between SO 2 and urinary level of t,t-MA (trans, trans-muconic acid) as a proxy-biomarker of NO 2 exposure, not merely benzene. However few studies related to NO 2 (0.46%) found detrimental effects. Overall, the number of studies reporting an association between air pollution and increased risk of ADHD is relatively higher compared to the number of studies reporting no association. However, the findings of the studies provided limited evidence to support the idea that exposure to air pollution may be linked to increased risk of ADHD. Well-designed and harmonized studies considering standard methods for individual exposure assessment, critical windows of susceptibility, and appropriate tools for outcome measurement, can improve the quality of epidemiological studies and strengthen the evidence. Since ADHD with its long-term consequences can impose large costs to communities and impact the children performance, determination of the risk factors in children and particularly the role of the environment as priorities for research should be considered. [ABSTRACT FROM AUTHOR]

How effective was the Kyoto protocol? International Environmental Agreements (IEA) have been on the rise over the past four decades; however, thus far their effectiveness is controversial. In view of the conflicting results found in the related literature, this paper addresses its effectiveness by utilizing for the first time the generalized synthetic control method (GSCM) to compare the emissions of the industrialized countries with a "No- Kyoto" counterfactual scenario that represents the expected emissions in the absence of the protocol. This method facilitates a robust comparison between treated and control countries as done by Almer and Winkler (2017) and account for the multiple treated units as done by Grunewald and Martinez-Zarzoso (2016), so as to capture the collective nature of the protocol. Results show that the protocol was successful in reducing the emissions of the ratifying countries approximately by 7% below the emissions expected under a "No-Kyoto" scenario, confirming the importance of accounting for the collective nature of the agreement. [ABSTRACT FROM AUTHOR]

Abstract The topic of air pollution has drawn considerable attention globally. In this paper, we examine the immediate effect of air pollution on a substantial decision, that is, a housing purchase. By linking housing purchasing behavior with the air quality in Beijing, we document market participants' behaviors unexplained by rational economic theories. Our main result suggests that the transaction prices on a severely polluted day are 0.65% higher than those of the days without pollution, other things being equal. This translates into approximately 3.51 million yuan daily increase based on the average transaction volume and price on a typical day in Beijing. The heterogeneity analysis further suggests that this effect is mostly driven by non-local and low income buyers. After ruling out rational explanations, we demonstrate that our empirical results are consistent with salience theory under weak assumptions. [ABSTRACT FROM AUTHOR]

Abstract Preterm birth (PTB) is a predictor of infant mortality and later-life morbidity. Despite recent declines, PTB rates remain high in the United States. Growing research suggests a possible relationship between a mother's exposure to common air pollutants, including fine particulate matter (PM 2.5), and PTB of her baby. Many policy actions to reduce exposure to common air pollutants require benefit-cost analysis (BCA), and it's possible that PTB will need to be included in BCA in the future. However, an estimate of the willingness to pay (WTP) to avoid PTB risk is not available, and a comprehensive alternative valuation of the health benefits of reducing pollutant-related PTB currently does not exist. This paper demonstrates an approach to assess potential economic benefits of reducing PTB resulting from environmental exposures when an estimate of WTP to avoid PTB risk is unavailable. We utilized a recent meta-analysis, county-level air quality data and county-level PTB prevalence data to estimate the potential health and economic benefits of a reduction in air pollution-related PTB, with PM 2.5 as our case study pollutant. Using this method, a simulated nationwide 10% decrease from 2008 PM 2.5 levels resulted in an estimated reduction of 5016 PTBs and benefits of at least $339 million, potentially reaching over one billion dollars when considering later-life effects of PTB. Highlights • Environmental contaminant exposures during pregnancy may contribute to preterm birth. • Preterm birth leads to increased near-term medical costs and later health effects. • Estimated 5016 preterm births avoided from 10% reduction in 2008 particulate matter. • Reduced preterm births result in estimated benefits of $339 million to $1 billion. • Methodology could potentially be used in future health benefits assessments. [ABSTRACT FROM AUTHOR]

Abstract The article presents the analysis results of the grouping of the European Union countries with regard to emission levels of gaseous pollutants (NH3, NMVOCs, SO x , NO x) and particulate air pollutants (PM10 and PM2.5) which are one of the most dangerous type of pollution for human health. Their long-lasting effects on the human body may negatively affect health and life expectancy. The analysis was based on the taxonomic method of grouping data using the K-means method which is a non-hierarchical method. The analysis used the data published by Eurostat. Organizing countries into categories of emissions of gaseous and dust pollutants to the atmosphere was conducted for four cases: the total level of emission, the level of emissions related to the GDP, the area, and per capita. The Euclidean distance was a measure of the distance between Member States. The obtained results indicate that, depending on the adopted criterion, there is a significant change in the composition of individual clusters. This confirms the assumptions of the Authors who claimed that in order to develop a more comprehensive analysis and assessment of the state of atmospheric pollution in EU countries it is necessary to include additional criteria, other than only the total emission of a given pollution. The objective of the research was to indicate that the analysis of emissions of selected type of pollution, and at the same time most threatening to human health, does not fully reflect the actual state of the problem when presented only in terms of the emitted amount. It is therefore appropriate for EU institutions to take more account of the differences between the individual countries in terms of the criteria presented in the paper, during the decision-making process concerning the sustainable development policy in the field of environmental protection. Highlights • Air pollution levels vary greatly amongst the EU countries. • Six pollutants occur in air are analysed. • For grouping objects (European Union countries) was used k-means method. • Presents the results of the analysis which takes into consideration the total emissions of air pollutants in relation to the four factors. [ABSTRACT FROM AUTHOR]

Thirty-three air samples were collected by high-volume samplers from May 2007 to June 2008 in the coastal area of southwest Taiwan and analyzed for total suspended particulates (TSP) and polycyclic aromatic hydrocarbons (PAHs). Concentrations of TSP and total PAHs ranged from 40.4 to 251 μg m−3 and 1.86–56.4 ng m−3, respectively. Except for joss paper burning during the religious celebration of Ghost Month, which resulted in the highest concentration of PAHs in the summer of 2007, a seasonal variation in total PAH concentration was observed over this study period, with the highest concentrations in winter and the lowest in summer. Because of the geographical and climatic characteristics of the sampling site, monsoon activities modulate the seasonal variations of PAHs. Diagnostic ratios showed that PAHs in the atmosphere of the Kaohsiung coastal area arose predominantly from vehicle emissions (mainly from diesel exhaust), joss paper burning, and coal/wood combustion. The results of hierarchical cluster analysis (HCA) and principal component analysis (PCA) indicated that the sampling days could be divided into three groups and that the major source identification of PAHs was the same as the identification by diagnostic ratios. In addition, the results of HCA and PCA suggest that the samples collected with a prevailing northerly or northeasterly wind direction contain both local emissions and those from neighboring sources. On the other hand, the cases related to westerly or northwesterly winds indicated that local emission was the major source for the sampling site. [Copyright &y& Elsevier]

Abstract Individuals take preventive measures to avoid costly air pollution exposure. This paper provides new empirical evidence of pollution avoidance that Chinese urban residents purchase particulate-filtering facemasks to protect against ambient air pollution. The analysis is conducted with detailed and comprehensive data available on daily facemask purchases and air quality that became available only very recently. We find that this transitory air pollution avoidance behavior exhibits dynamics and nonlinearities, with significant increases of facemask purchases during extreme pollution episodes. The daily model shows that a 100-point increase in Air Quality Index (AQI) increases the consumption of all masks by 54.5 percent and anti-PM 2.5 masks by 70.6 percent. The estimates from the aggregated model with flexible pollution levels are used to simulate the benefit of air quality improvement. If 10 percent of heavy pollution days (AQI ≥ 201) were eliminated, the total savings on facemasks alone would be approximately 187 million USD in China. This result suggests that reducing the occurrence of "airpocalypse" events represents a signifi1cant opportunity to improve social welfare. Nevertheless, our estimates are likely only a small part of the benefit of clean air because facemasks can only partially reduce the negative health effects of air pollution and the costs of other avoidance behaviors are not included. [ABSTRACT FROM AUTHOR]

Abstract Burning coal is known to have environmental costs; this paper demonstrates that the environmental costs of storing and handling coal are also sizable. We find that a 10% increase in the coal stockpiles held by U.S. power plants results in a 0.09% increase in average PM 2.5 concentration levels within 25 miles of these plants. Unlike most sources of variation in local air pollution, coal storage and handling impacts PM 2.5 but not other pollutants such as SO 2 and NO 2. Consequently, using coal stockpiles as an instrument, we show that a 10% increase in PM 2.5 causes a 1.1% (3.2%) increase in average adult (infant) mortality rates. Using a value of statistical life approach, our estimates indicate that a one ton increase in coal stockpiles results in local air pollution costs of $197. Economic policies that subsidize coal stockpiles highlight the importance of implementing environmental regulations specifically directed at coal storage and handling. [ABSTRACT FROM AUTHOR]

Abstract Little is known about the impact of air pollution on school children in developing countries. This paper aims to fill this gap by quantifying the causal effects of air pollution on the health status and the school attendance of the Chinese students. We relate the arguably exogenous daily variation in air pollution-instrumented by the occurrence of temperature inversion-with student illnesses and absences from more than 3000 schools in Guangzhou City. We find a sizable deleterious effect of air pollution on school attendance through the health channel. The impact persists for at least four days and displays a monotonically increasing pattern. Notably, this harmful effect is non-negligible even when pollution levels are below the official standards for air quality in China, suggesting that the current ambient air quality standards in China are not low enough to protect students. [ABSTRACT FROM AUTHOR]

European regulation 2016/427 (the first package of the so-called Real-Driving Emissions (RDE) regulation) introduced on-road testing with Portable Emissions Measurement Systems (PEMS) to complement the chassis dynamometer laboratory (Type I) test for the type approval of light-duty vehicles in the European Union since September 2017. The Not-To-Exceed (NTE) limit for a pollutant is the Type I test limit multiplied by a conformity factor that includes a margin for the additional measurement uncertainty of PEMS relative to standard laboratory equipment. The variability of measured results related to RDE trip design, vehicle operating conditions, and data evaluation remain outside of the uncertainty margin. The margins have to be reviewed annually (recital 10 of regulation 2016/646). This paper lays out the framework used for the first review of the NO x margin, which is also applicable to future margin reviews. Based on experimental data received from the stakeholders of the RDE technical working group in 2017, two NO x margin scenarios of 0.24–0.43 were calculated, accounting for different assumptions of possible zero drift behaviour of the PEMS during the tests. The reduced uncertainty margin compared to the one foreseen for 2020 (0.5) reflects the technical improvement of PEMS over the past few years. [ABSTRACT FROM AUTHOR]

To abate the severe air pollution, the Chinese government released the Action Plan of Air Pollution Prevention and Control (APAPPC) in 2013. This paper regards the APAPPC as a quasi-experiment and uses the DID method to investigate the impact of environmental regulation on the growth of green total-factor productivity of China's industries. This article employs the non-radial directional distance function (NDDF) and the global Malmquist index to measure the total-factor carbon productivity of China's industries. Further regressions suggest that the implementation of the APAPPC has significantly promoted the growth of the total-factor carbon productivity in the air pollution-intensive industries, and its marginal effect has steadily increased with time. This result is still valid after using a series of counterfactual tests and robustness tests. The further mechanism study shows that the APAPPC has significantly promoted R&D investment, especially in instruments and equipment, which has effectively promoted technical efficiency and technological advancement. It indicates that stringent and well-designed environmental regulations should lead to a "win-win" situation of environmental improvement and economic development by encouraging enterprises to upgrade their technology and equipment. • This paper uses a typical case in China to demonstrate the porter's hypothesis. • This paper uses the DEA method to measure the total-factor carbon productivity of China's industries. • The Action Plan of Air Pollution Prevention and Control in China has significantly promoted the growth of total-factor carbon productivity. • The regulation promotes productivity by propelling enterprises to increase R&D investment and update their technical equipment. [ABSTRACT FROM AUTHOR]