Effects Of Air Pollution Research Articles

Indoor and outdoor NO2 and SO2 levels in 13 randomly selected preschools from 7 districts in Mpumalanga Province, South Africa

AbstractBackgroundAir pollution is a serious worldwide issue, where both outdoor and indoor air quality have a substantial impact on the health of children. Children are more vulnerable to the effects of air pollution due to their developing respiratory systems and higher respiration rates than adults. These children dedicate a substantial amount of time to the preschool setting. The study sought to evaluate the concentration of NO2 and SO2 in both outdoor and indoor environments of preschool facilities.MethodsConcurrently, the levels of NO2 and SO2 were measured indoors and outdoors at 13 randomly selected preschools in 7 districts in Mpumalanga Province, South Africa. Radiello passive air samplers were employed to collect gaseous samples.ResultsThe mean levels of NO2 indoors and outdoors were within the recommended criteria outlined by the World Health Organization (WHO), specifically target levels 1–3. Additionally, these values were also below the air quality guides outlined by South Africa, both for yearly and 1‐h mean measurements. Nevertheless, both levels remained above the suitable threshold of 10 µg m−3 outlined by the WHO air quality guidelines.ConclusionsIt is important to implement proactive measures such as enhancing airflow systems, employing air filters and reducing potential sources of air pollution in preschools, to aid in reducing exposure to indoor and outdoor NO2 and SO2.

Long-term exposure to air pollution and road traffic noise and incidence of dementia in the Danish Nurse Cohort.

We examined the association of long-term exposure to air pollution and road traffic noise with dementia incidence in the Danish Nurse Cohort. Female nurses were followed for dementia incidence (hospital contact or medication prescription) from 1993/1999 to 2020. Air pollution and road traffic noise levels were estimated at nurses' residences, and their associations with dementia were examined using Cox regression models. Of 25,233 nurses 1409 developed dementia. Particulate matter with a diameter of ≤2.5µm (PM2.5) was associated with dementia incidence, after adjusting for lifestyle, socioeconomic status, and road traffic noise (hazard ratio [95% confidence interval] 1.35 [1.15-1.59] per interquartile range of 2.6µg/m3). There was no association of PM2.5 with dementia in physically active nurses. Association with road traffic noise diminished after adjusting for PM₂.₅ (1.02 [0.93-1.11] per 7.6dB). Long-term exposure to air pollution increases risk of dementia, and physical activity may moderate this risk. Long-term exposure to air pollution was associated with increased risk of dementia among female nurses from the Danish Nurse Cohort. Association of air pollution with dementia was independent of road traffic noise. Association of road traffic noise with dementia diminished after adjusting for air pollution. Physical activity moderated adverse effects of air pollution on dementia.

Long-term ambient air pollution and coronary atherosclerosis: Results from the Swedish SCAPIS study

Background and aimsDespite firm evidence for an association between long-term ambient air pollution exposure and cardiovascular morbidity and mortality, results from epidemiological studies on the association between air pollution exposure and atherosclerosis have not been consistent. We investigated associations between long-term low-level air pollution exposure and coronary atherosclerosis. MethodsWe performed a cross-sectional analysis in the large Swedish CArdioPulmonary bioImaging Study (SCAPIS, n = 30 154), a random general population sample. Concentrations of total and locally emitted particulate matter <2.5 μm (PM2.5), <10 μm (PM10), and nitrogen oxides (NOx) at the residential address were modelled using high-resolution dispersion models. We estimated associations between air pollution exposures and segment involvement score (SIS), coronary artery calcification score (CACS), number of non-calcified plaques (NCP), and number of significant stenoses, using ordinal regression models extensively adjusted for potential confounders. ResultsMedian 10-year average PM2.5 exposure was 6.2 μg/m3 (range 3.5–13.4 μg/m3). 51 % of participants were women and 51 % were never-smokers. None of the assessed pollutants were associated with a higher SIS or CACS. Exposure to PM2.5 was associated with NCP (adjusted OR 1.34, 95 % CI 1.13, 1.58, per 2.05 μg/m3). Associations with significant stenoses were inconsistent. ConclusionsIn this large, middle-aged general population sample with low exposure levels, air pollution was not associated with measures of total burden of coronary atherosclerosis. However, PM2.5 appeared to be associated with a higher prevalence of non-calcified plaques. The results suggest that increased risk of early-stage atherosclerosis or rupture, but not increased total atherosclerotic burden, may be a pathway for long-term air pollution effects on cardiovascular disease.

Specific biomass burning tracers in air pollution in Zagreb, Croatia

Biomass burning is considered a renewable energy source whose household usage is rapidly increasing throughout Europe, largely encouraged by the rising cost of other energy sources. With all the advantages that characterize such an energy source, there are several disadvantages mostly from the air quality standpoint, and consequently with regard to the influence it may have on human health and climate change. Considering the side effects of air pollution, it is important to identify the composition and sources of air pollutants precisely. This study investigated the presence of biomass combustion-specific tracers in the air of Zagreb, Croatia, a city affected by increased air pollution mostly during wintertime. Biomass burning markers in terms of anhydrosugars, namely levoglucosan, mannosan, and galactosan were monitored in particulate matter PM10. A sampling of PM10 particulate matter was conducted in parallel at measuring stations in the northern and southern part of the city during 30 days in each season in 2020. The results showed a significant seasonal difference in PM10 and anhydrosugar levels at both measuring stations with the same seasonal trend decreasing as follows: winter, autumn, spring, summer. Both PM10 and anhydrosugar concentrations were higher in the southern compared to the northern station. Average winter PM10 levels were 58 μg m−3 at the southern and 51 μg m−3 at the northern station, while average levoglucosan levels were 3.68 and 1.64 μg m−3 at the southern and northern stations, respectively. An average levoglucosan/PM10 contribution of 6.3 % was determined at the southern, and 3.2 % at the northern measuring station. The differences in contribution can be explained by areas more populated by households using biomass for residential heating, the dominant wind direction in the area, and influences from other districts, including long- or close-range transport. Linear regression indicated a weak correlation (R2 = 0.32) of anhydrosugars between the stations.

Effects of mimicking manure removal strategies on ammonia and greenhouse gas emissions in sow pen scale models

With growing concerns about air pollution and global warming effects, the emissions of NH3 and greenhouse gases (GHGs) have become significant issues in the pig production industry. In order to discover whether optimizing manure removal strategies could alter the manure decomposition process and benefit the reduction of gas emissions, a scale model approach was used to quantify the gas emissions under controlled conditions. This study compared the gas emission reduction potential of two classic manure removal systems (scraper and pull-plug system) in three manure removal strategies: scraper-I (retaining manure for 24 h), scraper-II (retaining manure for 8 and 16 h) and pull-plug (retaining manure for 44 days). Fresh urine and faeces collected from a gestation sow house were applied to the scale models and then removed through the manure removal systems. The concentrations of gases (NH3, CH4, CO2, and N2O) within the scale model and removed slurry samples were collected and analysed (pH, electric conductivity, dry matter, total nitrogen, total ammonium nitrogen and total carbon). The results showed that emissions of CH4, CO2, and N2O from scraper-I were on average 54%, 56% and 25% lower than those from scraper-II, and 71%, 67% and 6% lower than those from pull-plug treatment, respectively. The GHGs emission rates (as CO2-equivalents) of scraper-I and scraper-II were 52% and 26.3% lower than that of pull-plug treatment respectively (P < 0.01). The emissions of NH3 displayed a temporary peak during each application of urine and removal of manure. In pull-plug treatment, the concentrations of NH3, CH4, and CO2 exponentially increased between Day 39 and Day 41. The nitrogen content, both total nitrogen and total ammonium nitrogen, within the slurry under the scraper system exhibited lower values compared to those from the pull-plug system. Conversely, the total carbon content was higher in the former system. Caution is warranted in extrapolating results to full-scale pig housing, given study limitations (e.g. small scale, mimicked scraper activity, no animals, etc.). Nevertheless, the conclusions and findings of this study provide fundamental data for understanding gas emissions from pig house manure management. This insight can guide the design and daily operations of low-emission manure removal systems in gestation sow houses.

The short-term effects of individual and mixed ambient air pollutants on suicide mortality: A case-crossover study

It is critical to explore intervenable environmental factors in suicide mortality. Based on 30,688 suicide cases obtained from the Mortality Surveillance System of the Jiangsu Provincial Centre for Disease Control and Prevention, we utilized a case-crossover design, and found that the OR of suicide deaths increased by a maximum of 0.71 % (95 % CI: 0.09 %, 1.32 %), 0.68 % (95 % CI: 0.12 %, 1.25 %), 0.77 % (95 % CI: 0.19 %, 1.37 %), 2.95 % (95 % CI: 1.62 %, 4.29 %), 4.18 % (95 % CI: 1.55 %, 6.88 %), and 0.93 % (95 % CI: 0.10 %, 1.77 %), respectively, for per 10 μg/m3 increase in the particulate matter (PM) with diameters ≤ 2.5 µm (PM2.5), PM with diameters ≤ 10 µm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and per 0.1 mg/m3 increase in carbon monoxide (CO) concentrations with the conditional logistic regression analysis. People living in county-level cities were more susceptible. Particularly, a significant positive association was found between air pollutant mixture exposure and suicide deaths (OR=1.04,95 % CI: 1.01, 1.06). The excess fraction of suicide deaths due to air pollution reached a maximum of 8.07 %. In conclusion, we found associations between individual and mixed ambient air pollutants and suicide deaths, informing the development of integrated air pollution management and targeted measures for suicide prevention and intervention. Environmental ImplicationAs a major contributor to the global burden of disease, air pollution was confirmed by accumulating studies to have adverse impact on mental health, and potentially lead to suicide deaths. However, systematic studies on the association between air pollution and suicide mortality are lacking. We explored the associations of multiple air pollutants and pollution mixtures with suicide deaths and assessed excess suicide mortality due to air pollution, emphasizing the importance of air pollution control on suicide prevention. Our study provides evidence to support mechanistic studies on the association between air pollution and suicide, and informs comprehensive air pollution management.

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

Electricity consumption is expected to increase considerably in the next few years, so forecasting and planning will become more important. A new method of forecasting electricity loads based on air pollution is presented in this paper. Air pollution indirect effects are not incorporated in current evaluations since they rely primarily on weather conditions, historical load data, and seasonality. The accuracy of electricity load forecasting improved by incorporating air pollution data and its potential effects, especially in regions where air quality heavily impacts energy consumption and generation patterns. This robust prediction model is capable of capturing the complex interactions between air pollution and electricity load by integrating innovative environmental factors with historical load data, weather forecasts, and other features. As part of the second contribution, we use metaheuristic algorithms to optimize hyper parameters, which provide advantages such as exploration capability, global optimization, robustness, parallelization, and adaptability making them valuable tools to improve machine learning models’ performance and efficiency. The study found that the correlation coefficient (R) between predicted and real electricity demand and supply was high, at 0.9911. Beyond that this approach reduces MAPE by up to 19.5% when CNN and particle swarm optimization are combined with utilizing innovative air pollution variables. As a result, the optimization results were robust compared to state-of-the-art results based on statistical metrics such as RMSE and MAPE. Lastly, we emphasize the importance of factoring in air pollution effects when forecasting and managing electricity loads; future research directions include developing integrated modeling frameworks that reflect the dynamic interaction between air quality, energy consumption, and renewable energy generation.

Occupational particle exposure and chronic kidney disease: a cohort study in Swedish construction workers

ObjectivesIncreasing epidemiological and experimental evidence suggests that particle exposure is an environmental risk factor for chronic kidney disease (CKD). However, only a few case–control studies have investigated this association in...

Spatial synergistic effect of urban green space ecosystem on air pollution and heat island effect

It is crucial for urban greening studies to comprehend the spatial synergy effect of the urban green space ecosystem on air pollution and the heat island effect. However, there are limited studies that have considered the impact of green space ecosystems on different air pollutants and urban heat island effects across different seasons from an urban space perspective. Therefore, this study took an urban space perspective and established a high-precision spatial grid with a resolution of 500 m. The spatial econometric regression and bivariate local Moran's I test were employed to analyze the impacts of urban green space ecosystems on air pollutants (including summer ozone (O3) concentration, winter fine particulate matter (PM2.5) concentration and winter inhalable particulate matter (PM10) concentration) and heat islands in summer and winter.The results showed that the spatial regression coefficients between green space and heat islands, PM2.5 concentration and PM10 concentration are significantly negative, −0.00049***, −0.00329***, and −0.00365*, respectively. The urban green space ecosystems are observed to effectively mitigate the heat island effect and help to reduce PM2.5 and PM10 pollution in winter. There are 88 high-high clustering in the spatial interactions between green space and ozone concentration in summer, that is, the grids with high O3 concentration surrounded by the high green space grids. It implies that urban green space ecosystems can significantly increase O3 pollution in summer. This study reveals that low BVOCs tree species should be widely planted in urban green spaces, while large-scale green space parks should be established in the urban region to continuously increase the proportion of green space. This study would contribute to improving the awareness of the government and relevant employees on scientific urban greening methods.

A hybrid model for enhanced forecasting of PM2.5 spatiotemporal concentrations with high resolution and accuracy

Forecasting concentrations of PM2.5 is important due to its known impacts on public health and environment. However, PM2.5 concentrations can vary significantly over short distances and time, which can be influenced by local emissions and short-term weather patterns. This spatiotemporal variability makes accurate PM2.5 forecasting an inherently complex and challenging task. This study presented novel methodologies for short-term PM2.5 concentration forecast by combining the atmospheric chemistry transport model Community Multiscale Air Quality Modeling System (CMAQ) with data-driven machine learning methods, namely long short-term memory (LSTM) and random forest (RF) models. The combined model system forecast PM2.5 with 1 h, 1km × 1 km spatiotemporal resolution. The LSTM system forecast time-dependent PM2.5 concentrations at observation sites with a maximum root mean square error (RMSE) of 3.66 μg/m3 for 1-hr forecast and 23.75 μg/m3 for 72-hr forecast, leveraging results obtained from the atmospheric transport model with RMSE of 45.81 μg/m3. Wavelet transform in the LSTM system allowed learning and prediction of PM2.5 concentrations at different frequencies, capturing temporal variability of PM2.5 at various time scales. The RF model predicted distributions of PM2.5 concentrations by learning LSTM results and integrating crucial features such as CMAQ results, meteorological and topographical information. The feature significance of CMAQ results was the highest among the input features in RF models. Overall, the hybrid model could help with managing and mitigating the adverse effects of air pollution by enabling informed decision-making at the individual, community and policy levels.

Air Pollution and Mental Health: Evidence from China

We estimate the short-run effect of air pollution on mental illness. Using the China Family Panel Studies covering 12,615 urban residents during 2014-2015, we find a significantly positive effect of air pollution-instrumented by thermal inversions—on mental illness measured by the Kessler Psychological Distress Scale. Specifically, a 1 SD (18.04 mu-g/m3) increase in PM2.5 concentrations over a month increases the probability of having a score value associated with severe mental illness by 7.58 percentage points, or 0.37 SD. We find suggestive evidence that indirect channels—like reduced exercise and physical health—play a role in the effect.

Perception and Practices Regarding Stubble Burning in Rural Community of Haryana.

Agricultural emissions pose significant health risks, especially in countries like India with abundant agricultural waste. This study focuses on understanding stubble burning perceptions and practices in rural National Capital Region to inform targeted interventions for sustainable farming practices and improved public health. This community-based cross-sectional study was conducted among the randomly selected households of rural Ballabgarh, Haryana. A pre-tested, semi-structured questionnaire was used to obtain information on perception and practices on stubble burning. Data were collected in Epicollect 5 and analyzed in STATA 14. Of the 2000 households approached, 1813 responded with a response rate of 90.7%. Around 53% of the study participants believed that vehicular pollution is the main reason for air pollution, and 90% of them were unaware of the Government schemes related to stop stubble burning and with respect to the practices of stubble disposal. Around 70% of the participants mentioned that respiratory illness is the most severe effect of air pollution. The public was mostly unaware of the government's efforts to reduce stubble burning. Health promotion initiatives must be carried out to raise community knowledge about the programs available to combat stubble burning, therefore decreasing air pollution and its health implications.

Development of Measures to Reduce Air Pollutants As Much As Possible

The papers include contemporary ecological challenges, steps taken to reduce artificial pollution, and environmental issues relating to the atmosphere's circulation through natural and artificial processes. A major threat to world health, air pollution can have a range of negative consequences on the environment, such as haze, eutrophication, acid rain, and harm to animals. The combustion of fossil fuels for energy production and transportation is the main cause of air pollution. The effects of air pollution on human health are significant, with millions of people affected by respiratory and cardiovascular diseases, reproductive and central nervous system dysfunctions, and cancer. The articles emphasize the importance of reducing air pollution through various measures, such as improving energy efficiency, promoting renewable energy sources, reducing emissions from transportation, and implementing policies and regulations to control air pollution. The articles also highlight the need for public awareness and a multidisciplinary approach to tackle the problem of air pollution and its impact on climate change and public health.

The effect of indoor air filtration on biomarkers of inflammation and oxidative stress: a review and meta-analysis of randomized controlled trials.

Improvement of indoor air quality is beneficial for human health. However, previous studies have not reached consistent conclusions regarding the effects of indoor air filtration on inflammation and oxidative stress. This study aims to determine the relationship between indoor air filtration and inflammation and oxidative stress biomarkers. We conducted an electronic search that evaluated the association of indoor air filtration with biomarkers of inflammation and oxidative stress in five databases (PubMed, Cochrane Library, EMBASE, Web of Science, and Scopus) from the beginning to April 23, 2023. Outcomes included the following markers: interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), malondialdehyde (MDA), 8-hydroxy-2deoxyguanosine (8-OHdG), and 8-iso-prostaglandinF2α (8-isoPGF2α). We extracted data from the included studies according to the system evaluation and the preferred reporting item for meta-analysis (PRISMA) guidelines and used the Cochraneriskofbiastool to assess bias risk. Our meta-analysis included 15 studies with 678 participants to assess the combined effect size. The meta-analysis demonstrated that indoor air filtration could have a marked reduction in IL-6 (SMD: -0.275, 95% CI: -0.545 to -0.005, p = 0.046) but had no significant effect on other markers of inflammation or oxidative stress. Subgroup analysis results demonstrated a significant reduction in 8-OHdG levels in the subgroup with < 1 day of duration (SMD: -0.916, 95% CI: -1.513 to -0.320; p = 0.003) and using filtrete air filter (SMD: -5.530, 95% CI: -5.962 to -5.099; p < 0.001). Our meta-analysis results depicted that indoor air filtration can significantly reduce levels of inflammation and oxidative stress markers. Considering the adverse effects of air pollution on human health, our study provides powerful evidence for applying indoor air filtration to heavy atmospheric pollution.

Willingness of Park Users to Plant Trees in Public Motor Parks to Improve Environmental Condition in Ibadan Metropolis, Oyo State, Nigeria

The effect of global warming and air pollution have shifted more focus to tree planting as a way to combat climate change and improve the environment. Most Nigerian motor parks are polluted; thus, this study investigated the willingness of park users to plant trees in public motor parks to improve park's environmental condition in Ibadan Metropolis, Oyo State, Nigeria using structured questionnaires in six motor parks. Questionnaires were randomly administered to 12 drivers, 5 traders and 3 commuters, per Motor Park, making a total of 120 respondents. Study revealed that 60.8% of motor park users were under the age of 50; 100% of drivers were men, whereas women (70%) dominated trading. Drivers (94.4%) and traders (83.3%) use motor parks on a daily basis, but commuters (66.7%) use the park occasionally. Most of respondents (47.2%), traders (56.7%), and commuters (83.3%) stated that the park’s environment is not conducive (unsuitable). Respondents (85.8%) of motor park users indicated that they want a better environmental condition. Chi-square test reveals a significant relationship between respondents' educational status and perceived environmental condition of the motor park. Logistic analysis indicated that traders (0.183) and commuters (0.236) are less likely to be interested in tree planting. Most Nigerian motor parks have unsuitable environments, tree planting is one potential solution for improving the environmental condition. Relevant authorities should develop policies that encourage tree planting and tree planting should be incorporated and implemented in the design of new motor parks.

Predicting Air Quality

A list for revealing air quality is known as the air quality file (AQI). It measures the short-term effects of air pollution on a person's health. The motivation behind the AQI is to instruct general society on the negative well-being impacts of neighbourhood air contamination. How much air contamination in Indian urban communities has altogether expanded? The air quality index can be calculated mathematically in several different ways. Various examinations have found a connection between air contamination openness and unfavourable well-being influences in the populace. Data mining is one of the most intriguing methods for predicting and analysing AQI. This paper aims to determine the most efficient means by which AQI prediction can aid in climate control. The best technique can be enhanced to track down the most ideal arrangement. As a result, the work that is done in this paper requires extensive research as well as the addition of novel methods to guarantee that the best solution to the issue of poor air quality will be found. Another significant objective is to exhibit and show the specific measurements engaged with our work so that it is instructive and clever and subsequently gives legitimate examinations and helps future scientists. Keywords – Air Quality Index (AQI), Air pollution, Short-term effects, Health impacts, AQI prediction, Climate control, Novel methods, Extensive research, Comparative analysis, Future research.

Associations between short-term exposure to air pollution and thyroid function in a representative sample of the Korean population

BackgroundDisruption of thyroid function can profoundly affect various organ systems. However, studies on the association between air pollution and thyroid function are relatively scarce and most studies have focused on the long-term effects of air pollution among pregnant women. ObjectivesThis study aimed to explore the associations between short-term exposure to air pollution and thyroid function in the general population. MethodsData from the Korea National Health and Nutrition Examination Survey (2013–2015) were analyzed (n = 5,626). Air pollution concentrations in residential addresses were estimated using Community Multiscale Air Quality models. The moving averages of air pollution over 7 days were set as exposure variables through exploratory analyses. Linear regression and quantile g-computation models were constructed to assess the effects of individual air pollutants and air pollution mixture, respectively. ResultsA 10-ppb increase in NO2 (18.8-μg/m3 increase) and CO (11.5-μg/m3 increase) was associated with 2.43% [95% confidence interval (CI): 0.42, 4.48] and 0.19% (95% CI: 0.01, 0.36) higher thyroid-stimulating hormone (TSH) levels, respectively. A 10-μg/m3 increase in PM2.5 and a 10-ppb increase in O3 (19.6-μg/m3 increment) were associated with 0.87% (95% CI: 1.47, −0.27) and 0.59% (95% CI: 1.18, −0.001) lower free thyroxine (fT4) levels, respectively. A simultaneous quartile increase in PM2.5, NO2, O3, and CO levels was associated with lower fT4 but not TSH levels. ConclusionsAs the subtle changes in thyroid function can affect various organ systems, the present results may have substantial public health implications despite the relatively modest effect sizes. Because this was a cross-sectional study, it is necessary to conduct further experimental or repeated-measures studies to consolidate the current results.

Understanding the Cardiovascular and Metabolic Health Effects of Air Pollution in the Context of Cumulative Exposomic Impacts.

Poor air quality accounts for more than 9 million deaths a year globally according to recent estimates. A large portion of these deaths are attributable to cardiovascular causes, with evidence indicating that air pollution may also play an important role in the genesis of key cardiometabolic risk factors. Air pollution is not experienced in isolation but is part of a complex system, influenced by a host of other external environmental exposures, and interacting with intrinsic biologic factors and susceptibility to ultimately determine cardiovascular and metabolic outcomes. Given that the same fossil fuel emission sources that cause climate change also result in air pollution, there is a need for robust approaches that can not only limit climate change but also eliminate air pollution health effects, with an emphasis of protecting the most susceptible but also targeting interventions at the most vulnerable populations. In this review, we summarize the current state of epidemiologic and mechanistic evidence underpinning the association of air pollution with cardiometabolic disease and how complex interactions with other exposures and individual characteristics may modify these associations. We identify gaps in the current literature and suggest emerging approaches for policy makers to holistically approach cardiometabolic health risk and impact assessment.

Multiple xenoestrogen air pollutants and breast cancer risk: Statistical approaches to investigate combined exposures effect

Studies suggested that exposure to air pollutants, with endocrine disrupting (ED) properties, have a key role in breast cancer (BC) development. Although the population is exposed simultaneously to a mixture of multiple pollutants and ED pollutants may act via common biological mechanisms leading to synergic effects, epidemiological studies generally evaluate the effect of each pollutant separately. We aimed to assess the complex effect of exposure to a mixture of four xenoestrogen air pollutants (benzo-[a]-pyrene (BaP), cadmium, dioxin (2,3,7,8-Tétrachlorodibenzo-p-dioxin TCDD)), and polychlorinated biphenyl 153 (PCB153)) on the risk of BC, using three recent statistical methods, namely weighted quantile sum (WQS), quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR). The study was conducted on 5222 cases and 5222 matched controls nested within the French prospective E3N cohort initiated in 1990. Annual average exposure estimates to the pollutants were assessed using a chemistry transport model, at the participants’ residence address between 1990 and 2011. We found a positive association between the WQS index of the joint effect and the risk of overall BC (adjusted odds ratio (OR) = 1.10, 95% confidence intervals (CI): 1.03–1.19). Similar results were found for QGC (OR = 1.11, 95%CI: 1.03–1.19). Despite the association did not reach statistical significance in the BKMR model, we observed an increasing trend between the joint effect of the four pollutants and the risk of BC, when fixing other chemicals at their median concentrations. BaP, cadmium and PCB153 also showed positive trends in the multi-pollutant mixture, while dioxin showed a modest inverse trend. Despite we found a clear evidence of a positive association between the joint exposure to pollutants and BC risk only from WQS and QGC regression, we observed a similar suggestive trend using BKMR. This study makes a major contribution to the understanding of the joint effects of air pollution.

The cost of pollution in the digital era: Impediments of air pollution on enterprise digital transformation

The negative externalities of air pollution constrain the transformation of the real economy. Identifying the intrinsic association between air pollution and enterprise digital transformation (EDT) under the wave of the digital economy can help realize the synergistic promotion of green development and the digitalization process. The paper examines the impact of air pollution on EDT by matching the data of listed enterprises in Shanghai and Shenzhen, China, from 2010 to 2019 with city-level air pollution data. This paper uncovers the following findings: First, air pollution significantly hinders EDT. This finding remains robust after accounting for endogeneity, strategic disclosure in enterprise annual reports, and sample self-selection. Second, the effect of air pollution on EDT is asymmetric at the enterprise, industry, and regional levels. Specifically, air pollution exerts a more pronounced dampening effect on EDT in state-owned enterprises, heavily polluting industries, and low-marketization regions. Third, air pollution dampens EDT by increasing economic policy uncertainty external to the enterprise, reducing its internal talent reserve, and crowding out its digital investments. Fourth, augmenting the size of government subsidies, promoting digital infrastructure construction, and strengthening digital financial development are effective ways to mitigate air pollution's impediments to EDT. Fifth, in the face of severe air pollution, aggressive digital transformation strategies adopted by enterprises can alleviate financing constraints and enhance enterprise value. This paper effectively identifies the cost of pollution in the digital era from an EDT perspective, providing important policy insights for advancing EDT under air pollution.