Air Pollution From Forest and Vegetation Fires in Southeast Asia Disproportionately Impacts the Poor

Air pollution we breathe: Assessing the air quality and human health impact in a megacity of Southeast Asia

With 24 million inhabitants and 6.6 million vehicles on the roads, Karachi, Pakistan ranks among the world's most polluted cities due to high levels of fine particulate matter (PM2.5). This study aims to investigate PM2.5 mass, seasonal and temporal variability, chemical characterization, source apportionment, and health risk assessment at two urban sites in Karachi. Samples were analyzed using ion chromatography and dual-wavelength optical transmissometer for various inorganic components (anions, cations, and trace elements) and black carbon (BC). Several PM2.5 pollution episodes were frequently observed, with annual mean concentrations at Kemari (140 ± 179 μg/m3) and Malir (95 ± 40.9 μg/m3) being significantly above the World Health Organization's guidelines of 5 μg/m3. Chemical composition at both sites exhibited seasonal variability, with higher pollution levels in winter and fall and lower concentrations in summer. The annual average BC concentrations were 4.86 ± 5.29 μg/m3 and 4.52 ± 3.68 μg/m3, respectively. A Positive Matrix Factorization (PMF) analysis identified 5 factors, crustal, sea salt, vehicular exhaust, fossil-fuel combustion, and industrial emission. The health risk assessment indicated a higher number of deaths in colder seasons (fall and winter) at the Kemari (328,794 and 287,814) and Malir (228,406 and 165,737) sites and potential non-carcinogenic and carcinogenic risks to children from metals. The non-carcinogenic risk of PM2.5 bound Pb, Fe, Zn, Mn, Cr, Cu and Ni via inhalation exposure were within the acceptable level (<1) for adults. However, potential non-carcinogenic and carcinogenic health risk posed by Pb and Cr through inhalation were observed for children. The findings exhibit critical levels of air pollution that exceed the safe limits in Karachi, posing significant health risks to children and sensitive groups. Our study underscores the urgent need for effective emission control strategies and policy interventions to mitigate these air pollution risks.

Racial and ethnic minorities disproportionately exposed to extreme daily temperature variation in the United States

In the history of Homo sapiens, well-populated habitats have featured relatively stable temperatures with generally small daily variations. As the global population is increasingly residing in highly disparate climates, a burgeoning literature has documented the adverse health effects of single-day and day-to-day variation in temperature, raising questions of inequality in exposure to this environmental health risk. Yet, we continue to lack understanding of inequality in exposure to daily temperature variation (DTV) in the highly unequal United States. Using nighttime and daytime land surface temperature data between 2000 and 2017, this study analyzes population exposure to long-term DTV by race and ethnicity, income, and age for the 50 states and the District of Columbia. The analysis is based on population-weighted exposure at the census-tract level. We find that, on average, non-White (especially Black and Hispanic) and low-income Americans are exposed disproportionately to larger DTV. Race-based inequalities in exposure to DTV are larger than income-based disparities, with inequalities heightened in the summer months. In May, for example, the DTV difference by race and ethnicity of 51 states is between 0.20 and 3.01 °C (up to 21.0%). We find that younger populations are, on average, exposed to larger DTV, though the difference is marginal.

Prediction of air pollutant concentrations based on the long short-term memory neural network

In recent years, environmental problems caused by air pollutants have received increasing attention. Effective prediction of air pollutant concentrations is an important way to protect the public from harm. Recently, due to extreme climate and social development, the forest fire frequency has increased. During the biomass combustion process caused by forest fires, the content of particulate matter (PM) in the atmosphere increases significantly. However, most existing air pollutant concentration prediction methods do not consider the considerable impact of forest fires, and effective long-term prediction models have not been established to provide early warnings for harmful gases. Therefore, in this paper, we collected a daily air quality data set (aerodynamic diameter smaller than 2.5 µm, PM2.5) for Heilongjiang Province, China, from 2017 to 2023 and A novel Long Short-Term Memory (LSTM) model was proposed to effectively predict the situation of air pollutants. The model could automatically extract information of the effective time step from the historical data set and combine forest fire disturbance and climate data as auxiliary data to improve the model prediction ability. Moreover, we created artificial neural network (ANN) and permissive regression (support vector machine, SVR) models for comparative experiments. The results showed that the precision accuracy of the developed LSTM model is higher. Unlike the other models, the LSTM neural network model could effectively predict the concentration of air pollutants in long-term series. Regarding long-term observation missions (7 days), the proposed model performed well and stably, with R2 reaching over 88%.

Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model

With the potential to cause millions of deaths, PM2.5 pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM2.5 pollution and the existing PM2.5 studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM2.5 monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM2.5 concentrations. The proposed stacking model takes advantage of convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM) models and can well represent the spatiotemporal heterogeneity of the PM2.5-AOD relationship. In the cross-validation (CV), comparison with CNN and LightGBM models shows that the stacking model can better suppress overfitting, with a higher coefficient of determination (R2) of 0.92, a lower root mean square error (RMSE) of 5.58 μg/m3, and a lower mean absolute error (MAE) of 3.44 μg/m3. For the first time, the high-accuracy PM2.5 dataset reveals spatially and temporally continuous PM2.5 pollution and variations in the MRB from 2015 to 2022. Moreover, the spatiotemporal variations of annual and monthly PM2.5 pollution are also investigated at the regional and national scales. The dataset will contribute to the analysis of the causes of PM2.5 pollution and the development of mitigation policies in the MRB.

Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.

Impacts of different biomass burning emission inventories: Simulations of atmospheric CO2 concentrations based on GEOS-Chem

Biomass burning has substantial spatiotemporal variabilities. It contributes significantly to the dynamics of global CO₂ distributions and variances. Quantifying the impacts of biomass burning emissions on atmospheric CO₂ concentrations is essential for global and regional carbon cycles and budgets. In this study, we performed several numerical experiments by switching and replacing inventories to estimate the impacts of four biomass burning emission inventories on atmospheric CO₂ concentration simulations in 2006-2010 based on the global chemical transport model, GEOS-Chem. The results highlighted similarities and differences in the annual and seasonal variability of biomass burning emissions and simulated CO₂ concentrations at global and regional scales. Based on four different biomass burning emission inventories, we found that biomass burning emissions could lead to a global CO₂ concentration increase of 2.4 ppm annually. Africa contributed the largest global CO₂ emissions among all continental regions, where the maximum CO₂ concentration increase could reach 7.9-13.0 ppm in summer. Model evaluation results showed that simulation using the Quick Fire Emissions Database (QFED) as the model priori biomass burning emission inventory had the best performance compared with the satellite and surface observations. The sensitivity of simulated CO₂ concentrations to the uncertainties in different biomass burning emission inventories was high in southern South America and most areas of the Eurasian continent, and low in central Africa and Southeast Asia. This study furthers our understanding of the critical role of biomass burning in atmospheric CO₂ and indicates an urgent need to improve the accuracy of biomass burning emission estimates in CO₂ simulations.

Global biomass fires and infant mortality

Global outdoor biomass burning is a major contributor to air pollution, especially in low- and middle-income countries. Recent years have witnessed substantial changes in the extent of biomass burning, including large declines in Africa. However, direct evidence of the contribution of biomass burning to global health outcomes remains limited. Here, we use georeferenced data on more than 2 million births matched to satellite-derived burned area exposure to estimate the burden of biomass fires on infant mortality. We find that each additional square kilometer of burning is associated with nearly 2% higher infant mortality in nearby downwind locations. The share of infant deaths attributable to biomass fires has increased over time due to the rapid decline in other important causes of infant death. Applying our model estimates across harmonized district-level data covering 98% of global infant deaths, we find that exposure to outdoor biomass burning was associated with nearly 130,000 additional infant deaths per year globally over our 2004 to 2018 study period. Despite the observed decline in biomass burning in Africa, nearly 75% of global infant deaths due to burning still occur in Africa. While fully eliminating biomass burning is unlikely, we estimate that even achievable reductions-equivalent to the lowest observed annual burning in each location during our study period-could have avoided more than 70,000 infant deaths per year globally since 2004.

Quantifying the premature mortality and economic loss from wildfire-induced PM2.5 in the contiguous U.S

Emissions from wildfires worsen air quality and can adversely impact human health. This study utilized the fire inventory from NCAR (FINN) as wildfire emissions, and performed air quality modeling of April-October 2012, 2013, and 2014 using the U.S. Environmental Protection Agency CMAQ model under two cases: with and without wildfire emissions. This study then assessed the health impacts and economic values attributable to PM_2.5 from fires. Results indicated that wildfires could lead annually to 4000 cases of premature mortality in the U.S., corresponding to $36 billion losses. Regions with high concentrations of fire-induced PM_2.5 were in the west (e.g., Idaho, Montana, and northern California) and Southeast (e.g., Alabama, Georgia). Metropolitan areas located near fire sources, exhibited large health burdens, such as Los Angeles (119 premature deaths, corresponding to $1.07 billion), Atlanta (76, $0.69 billion), and Houston (65, $0.58 billion). Regions in the downwind of western fires, although experiencing relatively low values of fire-induced PM_2.5, showed notable health burdens due to their large population, such as metropolitan areas of New York (86, $0.78 billion), Chicago (60, $0.54 billion), and Pittsburgh (32, $0.29 billion). Results suggest that impacts from wildfires are substantial, and to mitigate these impacts, better forest management and more resilient infrastructure would be needed.

Global methyl halide emissions from biomass burning during 2003-2021

Methyl halides (CH₃Cl, CH₃Br, and CH₃I) are ozone-depleting substances. Biomass burning (BB) is an important source of methyl halides. The temporal variations and global spatial distribution of BB methyl halide emissions are unclear. Thus, global methyl halide emissions from BB during 2003-2021 were estimated based on satellite data. A significant decreasing trend (p < 0.01) in global methyl halide emissions from BB was found between 2003 and 2021, with CH₃Cl emissions decreasing from 302 to 220 Gg yr^-1, CH₃Br emissions decreasing from 16.5 to 11.7 Gg yr^-1, and CH₃I emissions decreasing from 8.9 to 6.1 Gg yr^-1. From a latitudinal perspective, the northern high-latitude region (60-90° N) was the only latitude zone with significant increases in BB methyl halide emissions (p < 0.01). Based on an analysis of the drivers of BB methyl halide emissions, emissions from cropland, grassland, and shrubland fires were more correlated with the burned area, while BB emissions from forest fires were more correlated with the emissions per unit burned area. The non-BB emissions of CH₃Cl increased from 4749 Gg yr^-1 in 2003 to 4882 Gg yr^-1 in 2020, while those of CH₃Br decreased from 136 Gg yr^-1 in 2003 to 118 Gg yr^-1 in 2020 (global total CH₃I emissions are not available). The finding indicates that global CH₃Cl and CH₃Br emissions from sources besides BB increased and decreased during 2003-2020. Based on our findings, not only searching for unknown sources is important, but also re-evaluating known sources is necessary for addressing methyl halide emissions.

Airborne particulate matter from biomass burning in Thailand: Recent issues, challenges, and options

Many of the current atmospheric environmental problems facing Thailand are linked to air pollution that is largely derived from biomass burning. Different parts of Thailand have distinctive sources of biomass emissions that affect air quality. The main contributors to atmospheric particulate matter (PM), especially the PM_2.5 fraction in Thailand, were highlighted in a recent study of PM derived from biomass burning. This review is divided into six sections. Section one is an introduction to biomass burning in Thailand. Section two covers issues related to biomass burning for each of the four main regions in Thailand, including Northern, Northeastern, Central, and Southern Thailand. In northern Thailand, forest fires and the burning of crop residues have contributed to air quality in the past decade. The northeast region is mainly affected by the burning of agricultural residues. However, the main contributor to PM in the Bangkok Metropolitan Region is motor vehicles and crop burning. In Southern Thailand, the impact of agoindustries, biomass combustion, and possible agricultural residue burning are the primary sources, and cross-border pollution is also important. The third section concerns the effect of biomass burning on human health. Finally, perspectives, new challenges, and policy recommendations are made concerning improving air quality in Thailand, e.g., forest fuel management and biomass utilization. The overall conclusions point to issues that will have a long-term impact on achieving a blue sky over Thailand through the development of coherent policies and the management of air pollution and sharing this knowledge with a broader audience.

Public Health Measures to Address the Impact of Climate Change on Population Health-Proceedings from a Stakeholder Workshop

BACKGROUND

The World Health Organization identified climate change as the 21st century's biggest health threat. This study aimed to identify the current knowledge base, evidence gaps, and implications for climate action and health policymaking to address the health impact of climate change, including in the most underserved groups.

METHODS

The Horizon-funded project ENBEL ('Enhancing Belmont Research Action to support EU policy making on climate change and health') organised a workshop at the 2021-European Public Health conference. Following presentations of mitigation and adaptation strategies, seven international researchers and public health experts participated in a panel discussion linking climate change and health. Two researchers transcribed and thematically analysed the panel discussion recording.

RESULTS

Four themes were identified: (1) 'Evidence is key' in leading the climate debate, (2) the need for 'messaging about health for policymaking and behaviour change' including health co-benefits of climate action, (3) existing 'inequalities between and within countries', and (4) 'insufficient resources and funding' to implement national health adaptation plans and facilitate evidence generation and climate action, particularly in vulnerable populations.

CONCLUSION

More capacity is needed to monitor health effects and inequities, evaluate adaptation and mitigation interventions, address current under-representations of low- or middle-income countries, and translate research into effective policymaking.

Concentrations and source identification of priority polycyclic aromatic hydrocarbons in sediment cores from south and northeast Thailand

In this study, the environmental fate of carcinogenic polycyclic aromatic hydrocarbons (PAHs) in tropical lake sediments and their potential sources have been discussed. 15 PAHs (i.e. ΣPAH) have been investigated in two lakes, namely Songkhla Lake (SKL) and Nong Han Kumphawapi Lake (NHL), which are located at the southern and north-eastern parts of Thailand, respectively. Since these two lakes are registered as important wetlands under the Ramsar convention (United Nations Educational, Scientific and Cultural Organization: UNESCO), the quantitative identification of potential contributors of PAHs is an inevitable analytical tool for launching an evidence-based policy. The ΣPAH concentrations observed in SKL and NHL sediments (n = 135) were in the range of 19.4–1,218 ng g⁻¹ and 94.5–1,112 ng g⁻¹, respectively. While the exponential decline of ΣPAH contents were detected in SKL sediments, NHL showed a trend of enhancing PAH contents with depth. The averaged benzo [a]pyrene (B [a]P) contents of surface sediments in both lakes were much below the value stipulated by the United States Environmental Protection Agency (US-EPA) guidelines for carcinogen risk assessment. Based on numerous multivariate statistical techniques coupled with source apportionment analysis, “biomass burning” and “anthropogenic activities” are two potential contributors of the PAHs detected in the study areas. To achieve the long-term conservation of nature with related ecosystem services and cultural values, it is therefore important to promote decision-making based on ecotoxicological studies of carcinogenic substances.

Air Quality, Pollution and Sustainability Trends in South Asia: A Population-Based Study

INTRODUCTION

Worsening air quality and pollution lead to numerous environmental health and sustainability issues in the South Asia region. This study analyzes India, Nepal, Bangladesh, Pakistan, Sri Lanka, and Nepal for air quality data trends and sustainability indicators.

METHODOLOGY

By using a population-based study design, six South Asian countries were analyzed using a step-wise approach. Data were obtained from government websites and publicly available repositories for region dynamics and key variables.

RESULTS

Between 1990 and 2020, air quality data indicated the highest rise in CO2 emissions in India (578.5 to 2441.8 million tons) (MT), Bangladesh, Nepal, and Pakistan. Greenhouse gas emissions, from 1990 to 2018, nearly tripled in India (1990.4 to 3346.6 MT of CO2-equivalents), Nepal (20.6 to 54.6 MT of CO2-equivalents), and Pakistan, and doubled in Bangladesh. Methane emissions rose the highest in Pakistan (70.4 to 151 MT of CO2-equivalents), followed by Nepal (17 to 31 MT of CO2-equivalents) and India (524.8 to 669.3 MT of CO2-equivalents). Nitrous oxide nearly doubled in Bangladesh (16.5 to 29.3 MT of CO2-equivalents), India (141.6 to 256.9 MT of CO2-equivalents), Nepal (17 to 31 MT of CO2-equivalents), and more than doubled in Pakistan (27 to 61 MT of CO2-equivalents). On noting particulate matter 2,5 annual exposure, India saw the highest rise from 81.3 µg/m3 (in 1990) to 90.9 µg/m3 (2017), whereas trends were steady in Pakistan (60.34 to 58.3 µg/m3). The highest rise was noted in Nepal (87.6 to 99.7 µg/m3) until 2017. During the coronavirus disease 19 pandemic, the pre-and post-pandemic changes between 2018 and 2021 indicated the highest PM2.5 concentration in Bangladesh (76.9 µg/m3), followed by Pakistan (66.8 µg/m3), India (58.1 µg/m3), Nepal (46 µg/m3) and Sri Lanka (17.4 µg/m3). Overall, South Asian countries contribute to the worst air quality and sustainability trends regions worldwide.

CONCLUSIONS

Air pollution is prevalent across a majority of South Asia countries. Owing to unsustainable industrial practices, pollution trends have risen to hazardous levels. Economic, environmental, and human health impacts have manifested and require urgent, concerted efforts by governing bodies in the region.

Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020

Anthropogenic emissions and ambient fine particulate matter (PM_2.5) concentrations have declined in recent years across China. However, PM_2.5 exposure remains high, ozone (O₃) exposure is increasing, and the public health impacts are substantial. We used emulators to explore how emission changes (averaged per sector over all species) have contributed to changes in air quality and public health in China over 2010-2020. We show that PM_2.5 exposure peaked in 2012 at 52.8 μg m^-3, with contributions of 31% from industry and 22% from residential emissions. In 2020, PM_2.5 exposure declined by 36% to 33.5 μg m^-3, where the contributions from industry and residential sources reduced to 15% and 17%, respectively. The PM_2.5 disease burden decreased by only 9% over 2012 where the contributions from industry and residential sources reduced to 15% and 17%, respectively 2020, partly due to an aging population with greater susceptibility to air pollution. Most of the reduction in PM_2.5 exposure and associated public health benefits occurred due to reductions in industrial (58%) and residential (29%) emissions. Reducing national PM_2.5 exposure below the World Health Organization Interim Target 2 (25 μg m^-3) would require a further 80% reduction in residential and industrial emissions, highlighting the challenges that remain to improve air quality in China.