Air Pollutant Concentrations Research Articles

Government-to-government peer pressure and air pollution: Causal evidence from an environmental ranking policy in China

This paper estimates the impact of peer governmental pressure on air quality using a regression discontinuity design inherent in China's air quality ranking system. Because air quality rankings are important for political evaluations, local officials who witnessed a decline in air quality ranking are motivated to enforce more stringent environmental standards compared to their counterparts with increased rankings. By exploiting this regulatory discontinuity with monthly air quality ranking data from 2012 to 2020 of 169 cities, we find that cities experiencing a lower air quality ranking in the current month compared to the previous month exhibit a subsequent improvement in air quality during the following month, as evidenced by decreased pollutant concentrations. Furthermore, our analysis reveals that in cities where pollutant rankings decline more sharply, officials are less likely to encounter fewer promotion opportunities. Importantly, we observe that the discontinuity in individual pollutant concentrations is not solely influenced by their respective rankings but is also impacted by the overall Air Quality Index (AQI) ranking. Linking the reduction in air pollutants concentration with the economic benefits, a back-of-the-envelope calculation indicates the decrease in premature deaths due to cardiovascular and respiratory diseases amounts to approximately 200 thousand, resulting in an associated economic saving of more than 600 billion Chinese yuan.

DEVELOPMENT OF LAND-USE REGRESSION MODELS FOR PARTICULATE MATTER ESTIMATION IN NATIONAL CAPITAL REGION, PHILIPPINES

Abstract. Regression models are commonly used to estimate unknown variables, such as environmental parameters. Multiple Linear Regression (MLR) is one of the techniques used to model air quality and measure air pollutant concentrations. Specifically, a technique called Land-Use Regression (LUR) enables the user to generate air pollutant models using geographical layers as input parameters. The study aims to generate models for fine and coarse particulate matter (PM2.5 and PM10, respectively) using LUR for the National Capital Region in 2019. Independent variables considered in this study are road network, traffic count, Normalized Difference Vegetation Index (NDVI), population density, and elevation. The final model results showed significant estimates based on the model parameters. For PM2.5, the model resulted in high values of R2 and adjusted R2 and an RMSE of 0.77 μg/m3. For PM10, model parameters showed that the generated final model for PM10 was significant with a 55% R2 value. Maps were then generated using the final LUR models of PM2.5 and PM10. The models can be improved by adding more types of input variables and longer observation periods.

Joint effect of long-term exposure to ambient air pollution on the prevalence of chronic obstructive pulmonary disease using the Korea National Health and Nutrition Examination Survey 2010–2019

BackgroundLittle is known about the relationship between long-term joint exposure to mixtures of air pollutants and the prevalence of chronic obstructive pulmonary disease (COPD). We aimed to assess the joint impact of long-term exposure to ambient air pollution on the prevalence of COPD in Korea, especially in areas with high levels of air pollution. MethodsWe included 22,387 participants who underwent spirometry tests in 2010–2019. The community multiscale air quality model was used to estimate the levels of ambient air pollution at residential addresses. The average exposure over the 5 years before the examination date was used to calculate the concentrations of air pollution. Forced expiratory volume in 1 s and forced vital capacity were used to define restrictive lung disease, COPD, and moderate-to-severe COPD. Quantile-based g-computation models were used to assess the joint impact of air pollution on COPD prevalence. ResultsA total of 2535 cases of restrictive lung disease, 2787 cases of COPD, and 1399 cases of moderate-to-severe COPD were identified. In the individual pollutant model, long-term exposure was significantly associated with both restrictive lung disease and COPD. In the mixture pollutant model, the odds ratios (ORs, 95% confidence intervals) for restrictive lung disease increased with each quartile increment in the 1- to 5-year average mixtures: 1.14 (1.02–1.28, 1 year), 1.25 (1.11–1.41, 2 years), 1.26 (1.11–1.42, 3 years), 1.32 (1.16–1.51, 4 years), and 1.37 (1.19–1.58, 5 years), respectively. The increase in ORs of restrictive lung disease accelerated over time. By contrast, the ORs of COPD showed a decreasing trend over time. ConclusionsLong-term exposure to air pollutants, both individually and jointly, was associated with an increased risk of developing COPD, particularly restrictive lung disease. Our findings highlight the importance of comprehensively assessing exposure to various air pollutants in relation to COPD.

SIMULATING THE EFFECTS OF EURO 4 DIESEL-POWERED ENGINE JEEPNEYS ON AIR QUALITY AND UNDERSTANDING ESTIMATED HEALTH RISK TOWARDS SUSTAINABLE TRANSPORTATION

Abstract. The Euro-4 diesel-powered engines are the current industry standard in the Philippines as indicated in the Public Utility Vehicle Modernization Program (PUVMP) Omnibus Guidelines Department Order 2017-011. Based on the National Emissions Inventory report of the Environmental Management Bureau in 2018, 74% of air pollutant concentrations were produced by mobile sources including jeepneys. The PUVMP is focused on enhancing environmental sustainability, air quality, and in providing a reliable and effective public transportation system. The potential advantages to air quality and health from the full implementation of the program especially the Euro 4 diesel-powered engine jeepneys in Trece Martires City are assessed in this paper. The study aims to estimate the impact of the possible enforcement of Euro 4 diesel-powered engine jeepneys on the reduction of air pollution in the research locale. Four pollutants were assessed particularly Carbon Monoxide (CO), Nitrogen Oxide (NOx), Sulfur Oxide (SOx) and Particulate Matter 2.5 (PM2.5) using AERMOD Air Dispersion Modeling Software in five (5) different scenarios: (1) baseline engine jeepneys, (2) 25% implementation of Euro 4 diesel-powered engine jeepneys, (3) 50% implementation of Euro 4 diesel-powered engine jeepney, (4) 75% implementation of Euro 4 diesel-powered engine jeepneys and (5) full implementation of Euro 4 diesel-powered engine jeepneys. AirQ+ was utilized to evaluate the health risk of Euro 4 diesel-powered engine jeepneys to the drivers and commuters in the vicinity. Result shows that the Scenario 5 or the full implementation of Euro 4 diesel-powered engines relatively reduces CO by 84.30% (8.355 μg/cu.m to 1.31 μg/cu.m), NOx by 21.76% (10.6 μg/cu.m to 8.27 μg/cu.m), and PM2.5 by 83.36% (2.69 μg/cu.m to 0.447 μg/cu.m), by its maximum estimated concentrations. However, an increase by 14.14% of SOx emissions (0.091 μg/cu.m to 0.104 μg/cu.m) were seen on the simulation from Scenario 1 to Scenario 5. In terms of health risk assessment, higher levels of PM2.5 exposure exhibit an increase in health hazard with respect to mortality rate. Simulation outputs indicate that transitioning of jeepneys from current technology to Euro 4 models reduces pollution levels and lowers health risk, hence, can be used as basis in enhancing transport plans and health monitoring strategies in the city.

Residual neural network with spatiotemporal attention integrated with temporal self-attention based on long short-term memory network for air pollutant concentration prediction

Accurate prediction of air pollutant concentrations can be effective in controlling and preventing air pollution, which is crucial for both the government's policy response and the public's reasonable ability to avoid air pollution. In this study, we propose a model based on the principle of big data correlation and deep learning techniques for predicting the concentration of air pollutant concentrations. The proposed model, named STARes-SaLSTM, consists of a residual neural network with spatiotemporal attention (STAResNet) and a temporal self-attention based on long short-term memory network (SaLSTM). The spatiotemporal attention module in STAResNet enhances the original input information by weighting it in both spatial and temporal dimensions, meanwhile the features of the spatial distribution of pollutant and meteorological information across many cities are deeply extracted using ResNet. A novel self-attention method known as temporal self-attention is developed to extract the temporal dependency of air pollutants. LSTM is used as a sequence encoder in this temporal attention method to calculate queries, keys, and values to acquire a more comprehensive temporal dependence than is possible with normal self-attention. The STARes-SaLSTM model successfully predicts air pollution concentrations in the target city for the future by extracting spatiotemporal correlation of feature sequence. In comparison to previous neural network models and conventional methods, the model increases the accuracy of pollutant concentration prediction. The suggested prediction model works well for the one to 3 h prediction job, with a root mean square error (RMSE) ranging from 6.716 to 11.648. Furthermore, even for the 1–24 h prediction assignment, we executed multi-scale prediction in the target city and obtained a reasonable performance, with an average RMSE value of 20.576.

PM<sub>2.5</sub> air pollution trends and patterns in Kandy, Sri Lanka

Air pollution is the leading environmental hazard that has devastating health effects on human health. Identification of trends and patterns of air pollution is crucial in a specific geographic area to inform and implement effective control and mitigation measurements. Standard air pollution monitoring networks are costly for most low-and middle-income countries. The aim of this study was to investigate the variations in the mean concentrations of air pollutants that occur at various times of the day, days of the week, months of the year, and seasons during the period of January 2019 to December 2019 in Kandy, Sri Lanka using low-cost sensors. A regression analysis with dummy variables was used to model the relationship between the concentrations of air pollutants and categorical independent variables, the time of day and day of the week. Considering temporal variations, the study found that pollution is highest in the early morning and evening, and lowest in the afternoon. The worst days for the air quality were Wednesdays and Thursdays, while Sundays had the lowest PM2.5 pollution. The analysis of air quality variations by month showed that December had the lowest air pollution of the examined months. The variation in PM2.5 was greatly influenced by wind speed, wind direction, and rainfall patterns. The study emphasized the importance of long-term consistently management and monitoring of air pollution levels.

Assessing the impact of air pollution on short-term hospital visits for respiratory diseases in Lampang, a city heavily affected by biomass burning in Mainland Southeast Asia: Sex and age considerations

Mainland Southeast Asia has long been confronted with severe air pollution issues caused by open biomass burning. The analysis is conducted from January 2014 to December 2017 in Lampang located in central of Mainland Southeast Asia. The analysis of air pollution revealed significant exceedances of the PM10 (32%) and O3 (29%) air quality guidelines set by the World Health Organization. The major source of PM10 in Lampang is biomass burning, accounting for 97%, and it also experiences a high respiratory disease incidence of 7,276.61 per 100,000 population. A time-series analysis was conducted using generalized linear models based on Poisson regression, considering various lag times, to assess the relative risk (RR) among all age groups (0–14 years, 15–64 years, ≥65 years) and genders. A 10 μg/m3 increase in PM10 and O3 was associated with statistically significant percentage increases at lag 0. Specifically, there was a 0.65% (95% CI: 0.38–0.92) increase in respiratory diseases for PM10 and a 0.72% (95% CI: 0.43–1.01) increase for O3. The impact of PM10 and O3 significantly affects respiratory diseases in children, adults, and the elderly. Elderly males and females both experience the highest impact of PM10, whereas the greatest impact of O3 is observed in elderly males and adult females. Biomass burning is a major source that should be controlled to reduce the health risk of people. Protecting vulnerable groups like adult females, elderly males, and elderly females from high air pollution concentrations is crucial, especially with many countries experiencing an aging demographic shift.

Study on the effect of residential interior decoration on children’s respiratory diseases: A field trial conducted in Chongqing, China

Pollutants such as formaldehyde, benzene series compounds and total volatile organic compounds (TVOC) released by residential interior decoration activities (including household renovation and new furniture) pose a severe threat to children’s respiratory health. To study the effect of indoor decoration on children’s respiratory diseases and provide suggestions to prevent it, we conducted a cross-sectional questionnaire survey amongst 6547 preschoolers from 34 kindergartens in Chongqing, China, and selected 39 houses to measure their indoor air pollutants concentrations. The results showed that household decoration, during pregnancy and early childhood, would increase the risk of children’s rhinitis, asthma and pneumonia. Vacancy time after the completion of decoration, furniture pieces and decoration materials significantly affected the concentration of formaldehyde, xylene and TVOC, which could pose a potential risk to children’s respiratory health. The concentration of xylene and TVOC and the incidence of children’s asthma and pneumonia were both negatively correlated with the vacancy time. Decoration materials such as composite and ceramic tile/stone/cement flooring and water-based/latex/oil-based paints emit more gaseous pollutants than solid wood flooring and wallpaper, respectively, which were more likely to increase the risk of children’s respiratory diseases. Adequate ventilation and air purification can reduce the impact of home decoration.

Ambient Air Pollution and Chronic kidney disease risk in Deltan communities: A Policy Brief, 2023

Chronic kidney disease (CKD) is a persistent, devastating, yet neglected, non-communicable disease in developing and emerging countries. National, regional, and international agencies’ communications and reports on non-communicable diseases intentionally or non-intentionally do not feature CKD. The traditional risk factors for CKD, such as hypertension and diabetes, which have received relatively ample attention, do not sufficiently explain the high burden of CKD in these countries. Ambient air pollution is an emerging significant environmental risk factor for CKD; however, epidemiological data and evidence are lacking for susceptible populations in developing countries. The Niger Delta region of Nigeria is a petrochemical hub known for environmental degradation, including air pollution, and thus, serves as a good case study for investigating the association between air pollution and CKD. This brief is based on the results of a mixed-methods study conducted in four communities situated near an oil and gas refinery in Warri, Nigeria. Air pollutant concentrations measured in partnership with citizen scientists showed that all except one air pollutant (ozone) exceeded the WHO acceptable limits in all four communities. The overall prevalence of CKD was high (12.3%) but even higher (18%) in a socially deprived semi-urban community closest to the oil refinery. Hypertension, diabetes, other behavioral risk factors, and exposures associated with CKD were prevalent among the inhabitants of the four communities. However, public environmental health information and education are lacking. A multifaceted approach is required to mitigate air pollution and the associated health risks in the state. Public inclusion is strongly recommended for the planning and implementation of future interventions. Kidney disease prevention and treatment should be emphasized in health policies and insurance schemes.

A Comparative Study of SOx, NOx, PM2.5 and PM10 in the UK and Poland from 1970 to 2020

Presently, there is growing discourse surrounding climate change, global warming, and the possibility of urban smog. Daily, we encounter air pollutants unwittingly, often oblivious to the substantial adverse impacts they may impart on our health. This manuscript furnishes a thorough examination of the shifts in the concentrations of distinct air pollutants, namely, sulphur oxides (SOx), nitrogen oxides (NOx), and particulate matter PM2.5 and PM10, within the contexts of the United Kingdom (a country progressively transitioning to alternative energy sources) and Poland (a country in the EU with the second lowest climate policy evaluation in the climate change performance index or CCPI) from 1970 to 2020. This comparative study between the two countries clearly showed the importance of the transition to clean energy sources. The UK’s efforts towards cleaner energy sources have led to reduced emissions of sulphur oxides (SOx) and particulate matter. In contrast, Poland’s heavy dependence on energy primarily from coal over the years has resulted in elevated pollution levels, notably in the concentration of pollutants such as particulate matter. This study also revealed that changes in emissions predominantly stem from technological advancements and economic activities. Additionally, political regulations and the gradual phase-out of specific fuels or energy-rich substances and their substitution with alternative energy sources have also imparted considerable influence.

The Relationship between Indoor and Outdoor Fine Particulate Matter in a High-Rise Building in Chicago Monitored by PurpleAir Sensors.

In urban areas like Chicago, daily life extends above ground level due to the prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between 8 April and 7 May 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 m away. The results show that outdoor PM2.5 concentrations peak at 14 m height, and then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 a.m. under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

The effect of workplace mobility on air pollution exposure inequality—a case study in the Central Belt of Scotland

A large number of epidemiological studies have identified air pollution as a major risk to human health. Exposures to the pollutants PM2.5, NO2 and O3 cause cardiovascular and respiratory diseases, cancer and premature mortality. Whilst previous studies have reported demographic inequalities in exposure, with the most deprived and susceptible often being disproportionately exposed to the highest pollutant concentrations, the vast majority of these studies have quantified exposure based only on individuals’ place of residence. Here we use anonymised personal data from UK Census 2011, and hourly modelled air pollution concentrations at 0.8 km × 1.4 km spatial resolution in the Central Belt of Scotland, to investigate how inclusion of time spent at place of work or study affects demographic inequalities in exposure. We split the population by sex, ethnic group, age and socio-economic status. Exposure gradients are observed across all demographic characteristics. Air pollution exposures of males are more affected by workplace exposures than females. The White ethnic group has the lowest exposures to NO2 and PM2.5, and highest to O3. Exposures to NO2 and PM2.5 tend to peak between the ages of 21 and 30, but those aged 31–50 tend to be most impacted by the inclusion of time spent at workplace in the exposure assessment. People in the two least deprived deciles consistently have the lowest residential-only and combined residential-workplace exposure to NO2 and PM2.5, but experience the highest increase in exposure when including workplace. Overall, including workplace exposure results in relatively small change in median exposure but attenuates some of the exposure inequalities associated with ethnicity and socioeconomic status observed in exposure assessments based only on place of residence.

Association of ambient air pollution with hemoglobin levels and anemia in the general population of Korean adults

BackgroundEmerging evidence has suggested significant associations between ambient air pollution and changes in hemoglobin levels or anemia in specific vulnerable groups, but few studies have assessed this relationship in the general population. This study aimed to evaluate the association between long-term exposure to air pollution and hemoglobin concentrations or anemia in general adults in South Korea.MethodsA total of 69,830 Korean adults from a large-scale nationwide survey were selected for our final analysis. Air pollutants included particulate matter with an aerodynamic diameter less than or equal to 10 micrometers (PM10), particulate matter with an aerodynamic diameter less than or equal to 2.5 micrometers, nitrogen dioxide, sulfur dioxide (SO2), and carbon monoxide (CO). We measured the serum hemoglobin concentration to assess anemia for each participant.ResultsIn the fully adjusted model, exposure levels to PM10, SO2, and CO for one and two years were significantly associated with decreased hemoglobin concentrations (all p < 0.05), with effects ranging from 0.15 to 0.62% per increase in interquartile range (IQR) for each air pollutant. We also showed a significant association of annual exposure to PM10 with anemia (p = 0.0426); the odds ratio (OR) [95% confidence interval (CI)] for anemia per each increase in IQR in PM10 was estimated to be 1.039 (1.001–1.079). This association was also found in the 2-year duration of exposure (OR = 1.046; 95% CI = 1.009–1.083; adjusted Model 2). In addition, CO exposure during two years was closely related to anemia (OR = 1.046; 95% CI = 1.004–1.091; adjusted Model 2).ConclusionsThis study provides the first evidence that long-term exposure to air pollution, especially PM10, is significantly associated with reduced hemoglobin levels and anemia in the general adult population.

Spatiotemporal Patterns of Air Pollutants over the Epidemic Course: A National Study in China

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM2.5, PM10, NO2, SO2, O3, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R2, demonstrated high accuracy with values of 0.92 for PM2.5, 0.95 for PM10, 0.95 for O3, 0.90 for NO2, 0.79 for SO2, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM2.5, although PM10 exhibited a rebound in northern regions. The concentrations of SO2 and CO consistently declined across the country over the epidemic’s course (p &lt; 0.001 and p &lt; 0.05, respectively), while O3 concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution.

Air pollutant concentration and population distribution economic planning simulation based on target tracking

China has emerged as one of the nations with the worst air pollution in recent years. The severe air pollution has caused a large number of population migration and also caused serious economic problems. Since the concentration of air pollutants can change quickly in a short amount of time, the study first tracked PM2.5, PM10, NO2, CO, SO2, and O3 as targets before using the particle swarm optimization algorithm to improve the PIO algorithm, which is based on the traditional pigeon swarm algorithm. To estimate the concentration of air pollutants, combine the wavelet packet decomposition technique, MDS visualization method, and k-means algorithm. Then, apply the enhanced PIO algorithm to optimize the ELM algorithm. Finally, a new type of decomposition-optimization-clustering-integration hybrid learning model, namely DOCIAPC model, is constructed. The experimental findings indicate that, when predicting the concentration of various air pollutants, the DOCIAPC model’s average direction prediction accuracy is 90.37% . In conclusion, the model suggested in the study has excellent performance and applicability, and it can accurately predict the concentration of air pollutants, help the government take action to reduce air pollution, balance the environment and economy, as well as the allocation of labor and its resources in the city.

Differentiating Periodic Drivers of Air Quality Changes: A Two‐Step Decomposition Approach Integrating Machine Learning and Wavelet Analysis

AbstractAir quality time series exhibit significant periodic patterns, which are linked to a diverse array of emission sources and atmospheric processes. To discern and distinguish these periodic drivers, we have devised a two‐step decomposition approach that integrates a machine learning‐based model for weather normalization with Morlet wavelet analysis. This approach was applied to a 7‐year data set encompassing six regulated air pollutants across eight Chinese cities. Our analysis revealed distinct periodicities in weather‐normalized concentrations of primary air pollutants: a dominant annual cycle with periodicity around 365 days, which accounts for over 50% of the variance on average and is primarily driven by recurrent winter heating activities; daily cycles characterized by regular diurnal patterns attributable to combustion sources such as traffic; and periodicities exceeding 512 days that associated with long‐term regulatory policies targeting SO2. Particularly notable was the significant drop in the strength of the annual cycle in northern cities following the implementation of China's clean heating policies in 2017/2018, affirming the success of these initiatives. Additionally, diurnal dispersion and photochemistry, large‐scale atmospheric circulation, and synoptical weather patterns are likely responsible for the observed daily cycle (accounting for over 40% of the variance), annual periodicities, and intra‐monthly variations in the meteorologically driven concentrations, respectively. The varying power of these periodic drivers across time and locations implies the heterogeneity of emission rates and region‐specific climates. This work highlights the efficacy of this decomposition approach in air quality research, warranting attention for its potential value for enhancing our understanding of air pollution dynamics.

How does air pollution damage residents’ well-being? The evidence from China

ABSTRACT Air pollution is a prevalent environmental problem facing the world, critically threatening the well-being of human beings. Using the China Genuine Progress indicator Survey dataset, we investigate the impact of air pollution on residents’ well-being in China, and discuss the influencing mechanisms based on the theory of hierarchy of needs. As distinct winter heating policies cause different concentrations of air pollutants across the Qinling-Huaihe line in China, we construct a regression discontinuity design to identify the causal relationship. The results show that air pollution significantly damages residents’ well-being. If annual average air quality index increases by 50 units, residents’ happiness will fall by about 1.774 units. The mechanism analysis shows that air pollution reduces individual utility through increasing residents’ daily expenditure to meet their low-level needs while reducing consumptions for higher-level needs, namely the so-called ‘consumption downgrade’. Heterogeneity analysis shows that residents with higher education levels, middle-income level, longer commute time, and stronger public awareness are more affected by air pollution. In sum, policymakers should highlight the impact of the environment on people’s subjective well-being and formulate appropriate intervention policies to alleviate adverse demographic changes that may harm the sustainable development.

Exploring the adverse effect of fine particulate matter (PM2.5) on wildland firefighters’ pulmonary function and DNA damage

Chiang Mai encounters severe pollution during the wildfire season. Wildland firefighters encounter various hazards while engaged in fire suppression operations, which encompass significant exposure to elevated concentrations of air pollutants resulting from combustion, especially particulate matter. The adverse effects of wildfire smoke on respiratory health are a significant concern. The objective of this study was to examine the potential adverse effects of PM2.5 exposure on the respiratory function and DNA damage of wildland firefighters. This prospective cohort study conducted in Chiang Mai from January to May 2022 planned to evaluate the health status of wildland firefighters during the pre-peak, peak, and post-peak ambient air pollution seasons. The measurement of PM2.5 was done at every forest fire station, as well as utilizing data from the Pollution Control Department. Participants received general health examinations, spirometry evaluations, and blood tests for DNA damage analysis. Pair t-tests and multiple regression models were used to examine the connection between pulmonary function parameters (FVC, FEV1) and PM2.5 concentration, with a significance level of P < 0.05. Thirty-three peak-season and twenty-one post-peak-season participants were enrolled. Four pre-peak-season wildland firefighters had FVC and FEV1 declines of > 15%. Multiple regression analysis showed a negative association between PM2.5 exposure and FVC% predicted (− 2.81%, 95% CI − 5.27 to − 0.34%, P = 0.027) and a marginally significant negative correlation with FVC (− 114.38 ml, 95% CI − 230.36 to 1.59 ml, P = 0.053). The remaining pulmonary measures showed a statistically insignificant decline. There were no significant changes in DNA damage detected. Wildland firefighters suffered a significant decline in pulmonary function associated with PM2.5 exposure. Spirometry is crucial for monitoring and promptly identifying respiratory issues that occur during wildfire seasons. Further research is recommended to explore DNA damage alterations and their potential association with PM2.5.

A comprehensive review of urban vegetation as a Nature-based Solution for sustainable management of particulate matter in ambient air.

Air pollution is one of the most pressing environmental threats worldwide, resulting in several health issues such as cardiovascular and respiratory disorders, as well as premature mortality. The harmful effects of air pollution are particularly concerning in urban areas, where mismanaged anthropogenic activities, such as growth in the global population, increase in the number of vehicles, and industrial activities, have led to an increase in the concentration of pollutants in the ambient air. Among air pollutants, particulate matter is responsible for most adverse impacts. Several techniques have been implemented to reduce particulate matter concentrations in the ambient air. However, despite all the threats and awareness, efforts to improve air quality remain inadequate. In recent years, urban vegetation has emerged as an efficient Nature-based Solution for managing environmental air pollution due to its ability to filter air, thereby reducing the atmospheric concentrations of particulate matter. This review characterizes the various mitigation mechanisms for particulate matter by urban vegetation (deposition, dispersion, and modification) and identifies key areas for further improvements within each mechanism. Through a systematic assessment of existing literature, this review also highlights the existing gaps in the present literature that need to be addressed to maximize the utility of urban vegetation in reducing particulate matter levels. In conclusion, the review emphasizes the urgent need for proper air pollution management through urban vegetation by integrating different fields, multiple stakeholders, and policymakers to support better implementation.

Assessing PM2.5 exposure bias towards deprived areas in England using a new indicator

In many countries higher concentrations of harmful air pollutants coincide with more deprived areas of society, where prevalence of health impacts associated with air pollution are greater. However, the impact of policies aimed at mitigating air pollution on the bias in exposure across deprivation groups rarely feature in policy development.We introduce the Indicator of Exposure Bias (IoEB) to the UK Integrated Assessment Model to quantify this bias, providing a method for comparing different scenarios and sectors. We analyse the bias in exposure to PM2.5 concentrations across deprivation levels within England currently and for future scenarios, and by sectors. While England's Index of Multiple Deprivation (IMD) and the Health domain are used as measures of deprivation here, the indicator can be applied to any pair of spatial socio-economic status and environmental exposure metrics.An IoEB of 0.88 µg.m−3 was calculated for the 2018 baseline, indicating a bias in exposures towards more deprived areas ranked using the IMD. All future scenarios considered here lead to reductions in population exposure and a reduction in the bias towards more deprived areas, with the greatest reduction of 59 % achieved by focusing on urban sources of primary PM2.5. The total bias in exposure towards more deprived areas is mitigated by an opposite bias for the transboundary contribution, therefore the bias in exposure of total PM2.5 does not accurately reflect that associated to UK anthropogenic emissions. The bias varies significantly between sectors, with non-industrial combustion and road transport the greatest contributors.Reaching England's 2040 PM2.5 exposure target is likely to deliver substantially greater health benefits for the more deprived members of society than the less deprived, reducing health inequality. The bias is sensitive to the measure of deprivation used, demonstrated using the Health domain.