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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]

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]

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]

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]

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]

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]

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]

Associations between fine particulate matter (PM2.5) exposure concentrations and a wide variety of undesirable outcomes, from autism and auto theft to elderly mortality, suicide, and violent crime, have been widely reported. Influential articles have argued that reducing National Ambient Air Quality Standards for PM2.5 is desirable to reduce these outcomes. Yet, other studies have found that reducing black smoke and other particulate matter by as much as 70% and dozens of micrograms per cubic meter has not detectably affected all-cause mortality rates even after decades, despite strong, statistically significant positive exposure concentration-response (C-R) associations between them. This paper examines whether this disconnect between association and causation might be explained in part by ignored estimation errors in estimated exposure concentrations. We use EPA air quality monitor data from the Los Angeles area of California to examine the shapes of estimated C-R functions for PM2.5 when the true C-R functions are assumed to be step functions with well-defined response thresholds. The estimated C-R functions mistakenly show risk as smoothly increasing with concentrations even well below the response thresholds, thus incorrectly predicting substantial risk reductions from reductions in concentrations that do not affect health risks. We conclude that ignored estimation errors obscure the shapes of true C-R functions, including possible thresholds, possibly leading to unrealistic predictions of the changes in risk caused by changing exposures. Instead of estimating improvements in public health per unit reduction (e.g., per 10 µg/m 3 decrease) in average PM2.5 concentrations, it may be essential to consider how interventions change the distributions of exposure concentrations. [ABSTRACT FROM AUTHOR]

Mounting evidence over the past several decades has demonstrated inequitable distribution of pollutants of ambient origin between sociodemographic groups in the United States. Most environmental inequality studies to date are cross-sectional and used proximity-based methods rather than modeled air pollution concentrations, limiting the ability to examine trends over time or the factors that drive exposure inequalities. In this paper, we use 1 km 2 modeled PM 2.5 and NO 2 concentrations in Massachusetts over an 8-year period and Census demographic data to quantify inequality between sociodemographic groups and to develop a more nuanced understanding of the drivers and trends in longitudinal air pollution inequality. Annual-average population-weighted PM 2.5 and NO 2 concentrations were highest for urban non-Hispanic black populations (11.8 µg/m 3 in 2003 and 8.4 µg/m 3 in 2010, vs. 11.3 µg/m 3 and 8.1 µg/m 3 for urban non-Hispanic whites) and urban Hispanic populations (15.9 ppb in 2005 and 13.0 ppb in 2010, vs. 13.0 ppb and 10.2 ppb for urban non-Hispanic whites), respectively. While population groups experienced similar absolute decreases in exposure over time, disparities in population-weighted concentrations increased over time when quantified by the Atkinson Index, a relative inequality measure. Exposure inequalities were approximately one order of magnitude greater for NO 2 compared to PM 2.5 , were more pronounced in urban compared to rural geographies, and between racial/ethnic groups compared to income and educational attainment groups. Our results also revealed similar longitudinal PM 2.5 and NO 2 inequality trends using Census 2000 and Census 2010 data, indicating that spatio-temporal shifts in air pollution may best explain observed trends in inequality. These findings enhance our understanding of factors that contribute to persistent inequalities and underscore the importance of targeted exposure reduction strategies aimed at vulnerable populations and neighborhoods. [ABSTRACT FROM AUTHOR]

In this paper we studied the exposure to three size fractions of outdoor particulate matter (PM 10 , PM 2.5 , and PM 1 ) collected in an area influenced by a cement plant. For that purpose, three groups of population were evaluated (children, adults and retired) in two seasons (summer and winter). Outdoor measured PM concentrations, as well as physiological parameters and activity patterns of the three groups of population were used as input data in two different models. The first one was an indoor air quality model, used to elucidate indoor PM concentrations in different microenvironments. The second one was a dosimetry model, used to evaluate the internal exposure and the distribution of the different PM fractions in the respiratory tract. Results from the indoor air quality model showed that special attention must be paid to the finest particles, since they penetrate indoors in a greater degree. Highest pulmonary doses for the three PM sizes were reported for retired people, being this a result of the high amount of time in outdoor environments exercising lightly. For children, the exposure was mainly influenced by the time they also spend outdoors, but in this case due to heavy intensity activities. It was noticed that deposition of fine particles was more significant in the pulmonary regions of children and retired people in comparison with adults, which has implications in the expected adverse health effects for those vulnerable groups of population. [ABSTRACT FROM AUTHOR]

Estimating health benefits from improvements in ambient air quality requires the characterization of the magnitude and shape of the association between marginal changes in exposure and marginal changes in risk, and its uncertainty. Several attempts have been made to do this, each requiring different assumptions. These include the L o g − L i n e a r (L L) , I n t e g r a t e d E x p o s u r e − Re s p o n s e (I E R) , and G l o b a l E x p o s u r e M o r t a l i t y M o d e l (G E M M). In this paper we develop an improved relative risk model suitable for use in health benefits analysis that incorporates features of existing models while addressing limitations in each model. We model the derivative of the relative risk function within a meta-analytic framework; a quantity directly applicable to benefits analysis, incorporating a F u s i o n of algebraic functions used in previous models. We assume a constant derivative in concentration over low exposures, like the L L model, a declining derivative over moderate exposures observed in cohort studies, and a derivative declining as the inverse of concentration over high global exposures in a similar manner to the G E M M. The model properties are illustrated with examples of fitting it to data for the six specific causes of death previously examined by the G l o b a l B u r d e n o f D i s e a s e program with ambient fine particulate matter (P M 2.5). In a test case analysis assuming a 1% (benefits analysis) or 100% (burden analysis), reduction in country-specific fine particulate matter concentrations, corresponding estimated global attributable deaths using the Fusion model were found to lie between those of the I E R and L L models, with the G E M M estimates similar to those based on the L L model. ∙ Fine particulate air pollution is one of the leading causes of death globally. ∙ Risk models relating exposure and disease are required to estimate health burden. ∙ Current risk models (IER, Log-Linear, GEMM) have limitations extrapolating risk either above or below observed health bases studies. ∙ A new risk model, based on the fusion of existing models, is proposed to address these limitations. ∙ The magnitude of predicted deaths using the Fusion model is in the middle of estimates from the Log-Linear and IER models. [ABSTRACT FROM AUTHOR]

COVID-19 is a lung disease, and there is medical evidence that air pollution is one of the external causes of lung diseases. Fine particulate matter is one of the air pollutants that damages pulmonary tissue. The combination of the coronavirus and fine particulate matter air pollution may exacerbate the coronavirus' effect on human health. This paper considers whether the long-term concentration of fine particulate matter of different sizes changes the number of detected coronavirus infections and the number of COVID-19 fatalities in Germany. Data from 400 German counties for fine particulate air pollution from 2002 to 2020 are used to measure the long-term impact of air pollution. Kriging interpolation is applied to complement data gaps. With an ecological study, the correlation between average particulate matter air pollution and COVID-19 cases, as well as fatalities, are estimated with OLS regressions. Thereby, socioeconomic and demographic covariates are included. An increase in the average long-term air pollution of 1 μg/m3 particulate matter PM 2.5 is correlated with 199.46 (SD = 29.66) more COVID-19 cases per 100,000 inhabitants in Germany. For PM 10 the respective increase is 52.38 (SD = 12.99) more cases per 100,000 inhabitants. The number of COVID-19 deaths were also positively correlated with PM 2.5 and PM 10 (6.18, SD = 1.44, respectively 2.11, SD = 0.71, additional COVID-19 deaths per 100,000 inhabitants). Long-term fine particulate air pollution is suspected as causing higher numbers of COVID-19 cases. Higher long-term air pollution may even increase COVID-19 death rates. We find that the results of the correlation analysis without controls are retained in a regression analysis with controls for relevant confounding factors. Nevertheless, additional epidemiological investigations are required to test the causality of particulate matter air pollution for COVID-19 cases and the severity. • COVID-19 infections are substantially positively correlated with long-term particulate matter concentration in German counties. • The correlation is robust to the inclusion of relevant control variables in multiple OLS regressions. • COVID-19 fatalities are also positively correlated with long-term particulate matter concentration. • The correlation between particulate matter concentration and COVID-19 fatalities was not robust. [ABSTRACT FROM AUTHOR]

The effectiveness of air pollution emission control policies can be evaluated by examining ambient pollutant concentration trends that are observed at a large number of ground monitoring sites over time. In this paper, we used ground monitoring measurements in conjunction with satellite aerosol optical depth (AOD) data to investigate fine particulate matter (PM 2.5 ; particulate matter with aerodynamic diameter≤2.5 µm) trends and their spatial patterns over a large U.S. region, New England, during 2000–2008. We examined the trends in rural and urban areas to get a better insight about the trends of regional and local source emissions. Decreases in PM 2.5 concentrations (µg/m 3 ) were more pronounced in urban areas than in rural ones. In addition, the highest and lowest PM 2.5 decreases (µg/m 3 ) were observed for winter and summer, respectively. Together, these findings suggest that primary particle concentrations decreased more relative to secondary ones. This is also supported by the analysis of the speciation data which showed that downward trends of primary pollutants including black carbon were stronger than those of secondary pollutants including sulfate. Furthermore, this study found that ambient primary pollutants decreased at the same rate as their respective source emissions. This was not the case for secondary pollutants which decreased at a slower rate than that of their precursor emissions. This indicates that concentrations of secondary pollutants depend not only on the primary emissions but also on the availability of atmospheric oxidants which might not change during the study period. This novel approach of investigating spatially varying concentration trends, in combination with ground PM 2.5 species trends, can be of substantial regulatory importance. [ABSTRACT FROM AUTHOR]

The questioned link between air pollution and coronavirus disease 2019 (COVID-19) spreading or related mortality represents a hot topic that has immediately been regarded in the light of divergent views. A first "school of thought" advocates that what matters are only standard epidemiological variables (i.e. frequency of interactions in proportion of the viral charge). A second school of thought argues that co-factors such as quality of air play an important role too. We analyzed available literature concerning the link between air quality, as measured by different pollutants and a number of COVID-19 outcomes, such as number of positive cases, deaths, and excess mortality rates. We reviewed several studies conducted worldwide and discussing many different methodological approaches aimed at investigating causality associations. Our paper reviewed the most recent empirical researches documenting the existence of a huge evidence produced worldwide concerning the role played by air pollution on health in general and on COVID-19 outcomes in particular. These results support both research hypotheses, i.e. long-term exposure effects and short-term consequences (including the hypothesis of particulate matter acting as viral "carrier") according to the two schools of thought, respectively. The link between air pollution and COVID-19 outcomes is strong and robust as resulting from many different research methodologies. Policy implications should be drawn from a "rational" assessment of these findings as "not taking any action" represents an action itself. [ABSTRACT FROM AUTHOR]

Long-term exposure to air pollution has been related to mortality in several epidemiological studies. The investigations have assessed exposure using various methods achieving different accuracy in predicting air pollutants concentrations. The comparison of the health effects estimates are therefore challenging. This paper aims to compare the effect estimates of the long-term effects of air pollutants (particulate matter with aerodynamic diameter less than 10 μm, PM 10 , and nitrogen dioxide, NO 2) on cause-specific mortality in the Rome Longitudinal Study, using exposure estimates obtained with different models and spatial resolutions. Annual averages of NO 2 and PM 10 were estimated for the year 2015 in a large portion of the Rome urban area (12 × 12 km2) applying three modelling techniques available at increasing spatial resolution: 1) a chemical transport model (CTM) at 1km resolution; 2) a land-use random forest (LURF) approach at 200m resolution; 3) a micro-scale Lagrangian particle dispersion model (PMSS) taking into account the effect of buildings structure at 4 m resolution with results post processed at different buffer sizes (12, 24, 52, 100 and 200 m). All the exposures were assigned at the residential addresses of 482,259 citizens of Rome 30+ years of age who were enrolled on 2001 and followed-up till 2015. The association between annual exposures and natural-cause, cardiovascular (CVD) and respiratory (RESP) mortality were estimated using Cox proportional hazards models adjusted for individual and area-level confounders. We found different distributions of both NO 2 and PM 10 concentrations, across models and spatial resolutions. Natural cause and CVD mortality outcomes were all positively associated with NO 2 and PM 10 regardless of the model and spatial resolution when using a relative scale of the exposure such as the interquartile range (IQR): adjusted Hazard Ratios (HR), and 95% confidence intervals (CI), of natural cause mortality, per IQR increments in the two pollutants, ranged between 1.012 (1.004, 1.021) and 1.018 (1.007, 1.028) for the different NO 2 estimates, and between 1.010 (1.000, 1.020) and 1.020 (1.008, 1.031) for PM 10 , with a tendency of larger effect for lower resolution exposures. The latter was even stronger when a fixed value of 10 μg/m3 is used to calculate HRs. Long-term effects of air pollution on mortality in Rome were consistent across different models for exposure assessment, and different spatial resolutions. • Chemical Transport, Random Forest and Lagrangian particle models used for exposure. • Building effects and city structure considered at different buffer sizes. • NO 2 and PM 10 long-term health effects evaluated by different models and resolution. • Significant associations with natural and cardiovascular mortality shown by all models. • Hazard Ratios for increments of interquartile ranges are not statistically different. [ABSTRACT FROM AUTHOR]

The severe cases of COVID-19 infections in Italy, and notably in Lombardy (mainly in Brescia and Bergamo areas), registered at the beginning of March 2020, occurred after a period of PM 10 pollution, that exceeded the concentration of 50 μg/m3 (the attention limit) for several days. The two events were supposed to be correlated, also based on the limited information available about the new virus. Despite that clear indications about the role of particulate matter (PM) in the virus mechanism dispersion cannot be found in literature, some researchers supposed that PM can act as virus carrier, promoting its diffusion through the air. This paper, for the first time, analyses the PM 10 situation in Lombardy (from 10th February to March 27, 2020), several days before the sanitary emergency explosion. The data of the detected infection cases are reported and discussed parallelly. As a comparison, the situation of Piedmont, located near to the Lombardy is also presented. Data are reported for Brescia, Bergamo, Cremona, Lodi, Milano, Monza-Brianza, Pavia (Lombardy), Alessandria, Vercelli, Novara, Biella, Asti, and Torino (Piedmont). The results show that it is not possible to conclude that COVID-19 diffusion mechanism also occurs through the air, by using PM 10 as a carrier. In particular, it is shown that Piedmont cities, presenting lower detected infections cases in comparison to Brescia and Bergamo in the investigated period, had most sever PM 10 pollution events in comparison to Lombardy cities. This first study may serve as a reference to better understand and predict the factors affecting the COVID-19 diffusion and transmission routes, focusing on the role of air particulate matter in the atmosphere. • PM 10 situation during COVID-19 sanitary crisis is analysed in Lombardy (Italy). • The possible correlation with the reported infection cases is discussed. • The role of airborne PM for the virus diffusion is not evident. [ABSTRACT FROM AUTHOR]

Previous studies have cataloged social disparities in air pollution exposure in US public schools with respect to race/ethnicity and socioeconomic status. These studies rely upon chronic, averaged measures of air pollution, which fosters a static conception of exposure disparities. This paper examines PM 2.5 exposure disparities in Salt Lake County (SLC), Utah public schools under three different PM 2.5 scenarios—relatively clean air, a moderate winter persistent cold air pool (PCAP), and a major winter PCAP—with respect to race/ethnicity, economic deprivation, student age, and school type. We pair demographic data for SLC schools (n = 174) with modelled PM 2.5 values, obtained from a distributed network of sensors placed through a community-university partnership. Results from generalized estimating equations controlling for school district clustering and other covariates reveal that patterns of social inequality vary under different PM 2.5 pollution scenarios. Charter schools and schools serving economically deprived students experienced disproportionate exposure during relatively clean air and moderate PM 2.5 PCAP conditions, but those inequalities attenuated under major PCAP conditions. Schools with higher proportions of racial/ethnic minority students were unequally exposed under all PM 2.5 pollution scenarios, reflecting the robustness of racial/ethnic disparities in exposure. The findings speak to the need for policy changes to protect school-aged children from environmental harm in SLC and elsewhere. • We examined social disparities in PM 2.5 levels at Salt Lake County schools. • A community-university partnership provided PM 2.5 measures from ~190 sensors. • Social disparities in school-based PM 2.5 exposure varied under 3 different scenarios. • Charter schools were disproportionately exposed when PM 2.5 was low and moderate. • Predominantly minority schools were disproportionately exposed under all scenarios. [ABSTRACT FROM AUTHOR]

Much of the key epidemiological evidence that long-term exposure to fine particulate matter air pollution (PM 2.5) contributes to increased risk of mortality comes from survival studies of cohorts of individuals. Although the first two of these studies, published in the mid-1990s, were highly controversial, much has changed in the last 25 + years. The objectives of this paper are to succinctly compile and summarize the findings of these cohort studies using meta-analytic tools and to address several of the key controversies. Independent reanalysis and substantial extended analysis of the original cohort studies have been conducted and many additional studies using a wide variety of cohorts, including cohorts constructed from public data and leveraging natural experiments have been published. Meta-analytic estimates of the mean of the distribution of effects from cohort studies that are currently available, provide substantial evidence of adverse air pollution associations with all-cause, cardiopulmonary, and lung cancer mortality. • Cohort studies link PM 2.5 air pollution with excess mortality risk. • 25+ years of re-analyses, extended analyses, many replicative analyses. • Evidence from many studies from North America, Europe, and Asia. • Meta-estimates indicate robust PM 2.5 -mortality associations with heterogeneity. • PM 2.5 associated with all-cause, cardiopulmonary and lung-cancer mortality. [ABSTRACT FROM AUTHOR]

Air pollutants are suggested to be related to type 2 diabetes (T2D). Since several high quality papers on air pollutants and T2D have been published beyond the last reviews, an extended systematic review is highly warranted. We review epidemiological studies to quantify the association between air pollutants and T2D, and to answer if diabetes patients are more vulnerable to air pollutants. We systematically reviewed the databases of PubMed and Web of Science based on the guidelines of the Preferred Reporting Items for Systematic review and Meta-analysis (PRISMA). We calculated odds ratios (OR) or hazard ratios (HR) and their 95% confidence intervals (CI) to assess the strength of the associations between air pollutants [e.g., particulate matter with diameter ≤ 2.5 μm (PM 2.5), particulate matter with diameter ≤ 10 μm (PM 10), and nitrogen dioxide (NO 2)] and T2D. We evaluated the quality and risk of bias of the included studies and graded the credibility of the pooled evidence using several recommended tools. We also performed sensitivity analysis, meta-regression analysis, and publication bias test. Out of 716 articles identified, 86 were used for this review and meta-analysis. Meta-analyses showed significant associations of PM 2.5 with T2D incidence (11 studies; HR = 1.10, 95% CI = 1.04–1.17 per 10 μg/m3 increment; I2 = 74.4%) and prevalence (11 studies; OR = 1.08; 95% CI = 1.04–1.12 per 10 μg/m3 increment; I2 = 84.3%), of PM 10 with T2D prevalence (6 studies; OR = 1.10; 95% CI = 1.03–1.17 per 10 μg/m3 increment; I2 = 89.5%) and incidence (6 studies; HR = 1.11; 95% CI = 1.00–1.22 per μg/m3 increment; I2 = 70.6%), and of NO 2 with T2D prevalence (11 studies; OR = 1.07; 95% CI = 1.04–1.11 per 10 μg/m3 increment; I2 = 91.1%). The majority of studies on glucose-homoeostasis markers also showed increased risks with higher air pollutants levels, but the studies were too heterogeneous for meta-analysis. Overall, patients with diabetes might be more vulnerable to PM. Recent publications strengthened the evidence for adverse effects of ambient air pollutants exposure (especially for PM) on T2D and that diabetic patients might be more vulnerable to air pollutants exposure. • Evidence on ambient air pollution and diabetes is still inconsistent. • We reviewed 86 studies on air pollution and diabetes and its traits. • We meta-analyzed 29 studies for PM and NO2 with diabetes. • Air pollutants (e.g., PM and NO2) are associated with higher odds of diabetes. • Air pollution is associated with dysregulation of glucose metabolism. [ABSTRACT FROM AUTHOR]

Exposure to ultrafine particulate air pollution (UFP) contributes to adverse health effects in sensitive population groups such as children. There is a need to explore UFP exposure in terms of respiratory dosage, which leverages the effect of activity-specific ventilation rates. Commute-related dosage, which describes the dosage that occurs during travel between fixed locations on a routine schedule (i.e. morning school commute), is often underrepresented in air pollution studies. School commutes commonly utilize active transportation modes, and modelling dosage during such commutes requires the development of an approach that captures the influence of travel mode and terrain-enforced ventilation rates. The concept of low-dosage routing is discussed as a solution to UFP dose mitigation; however, it requires an in-depth understanding of the factors that control how dosage is accumulated at different points in the commute. This paper presents a modelling workflow for examining UFP dosage while walking to school. We apply a GIS-based approach that simulates school commute routes for 296,862 homes in Toronto. We use a physical exertion–dependent dosage model that reflects broad-scale variability in physiology, travel velocity, travel gradient, and ambient UFP concentration to evaluate route-specific commute dosage. Our results reveal the patterns of within-route variation in dosage and demonstrate the effects high regional UFP concentration and accelerated physical exertion (i.e. where ventilation rate increases during uphill travel) on creating sections of greater UFP dosage within a route. We use the model to examine UFP dosage under shortest-distance and lowest-dosage routing solutions and find that 13.4% of homes had low-dosage alternative routes, while 86.6% had shortest-distances route that were already the lowest-dosage. Our findings were consistent with studies in the literature that implement a dosage modelling approach. This study highlights the importance of dosage models that account for the dynamic nature of walking speed, ventilation rate, terrain, and pollution concentration along a school commute. • Travel-gradients affect respiratory dosage of ultrafine particulate matter. • Dosage is accelerated during school-commutes with greater physical exertion. • Some children who walk to school could benefit from low-dosage routing optimization. • The shortest route is likely the lowest-dosage route for most children in Toronto. [ABSTRACT FROM AUTHOR]