Criteria Air Pollutants Research Articles

Development of Land Use Regression (LUR) models and high-resolution spatial mapping of criteria air pollutants: Leveraging Delhi's continuous air monitoring network and remote sensing data

Development of Land Use Regression (LUR) models and high-resolution spatial mapping of criteria air pollutants: Leveraging Delhi's continuous air monitoring network and remote sensing data

EVALUATION OF PLANTS BASED ON ECOLOGICAL TOLERANCE CRITERIA: A CASE STUDY OF URBAN OPEN GREEN SPACES IN RIZE, TURKIYE

In addition to aggressive urbanization, climate change and ecological challenges also have serious negative effects on urban green areas. Moreover, the selection of inappropriate plants in urban green space landscape projects increases the severity of these negatives. Therefore, inappropriate plant selection is considered a significant threat to the sustainability of urban green spaces. This situation highlights the importance of selecting plants with high ecological tolerance in urban park landscape projects. In this study, all tree, shrub, and ground cover plants identified in 11 urban parks and open green areas in Rize province were evaluated in terms of ecological tolerance criteria (air pollution, drought, wind, temperature, cold, and salinity). A total of 223 plant taxa were examined (77 native, 3 semi-native, 143 exotic), and it was determined that 149 taxa (49 native, 100 exotic) were sensitive to at least one of these criteria, while the remaining 74 taxa (27 native, 47 exotic) had sufficient tolerance to all ecological variables examined. Prunus serrulata Lindl. ‘Kanzan’ was identified as the taxon with the weakest ecological tolerance, and the resilience statuses of the other taxa were presented in the assessment table created. As a result, it was found that there are taxa used in Rize urban open green areas that pose risks against ecological factors, and recommendations were made for future landscape planting projects.

Criteria air pollutants and HIV-related mortality: Insights from a nationwide case-crossover investigation

The heightened vulnerability of individuals with HIV to environmental stressors is well-recognized, yet the role of air pollution in exacerbating HIV-related mortality remains underexplored. In this nationwide, individual-level case-crossover study conducted from 2013 to 2019, we investigated the association between short-term exposure to criteria air pollutants and HIV-related mortality. Our analysis of 38,510 HIV-related deaths revealed significant associations between exposure to PM2.5, PM10, NO2, and CO and increased mortality risk. The effects of PM2.5 and PM10 persisted for two days, whereas NO2 and CO had immediate, same-day impacts. Vulnerability was heightened in individuals under 65 years, males, those with lower educational attainment, and unmarried individuals. Among causes of death, HIV-related malignant neoplasms exhibited the highest sensitivity to particulate matter. Our findings provide novel insights into the relationship between short-term air pollution exposure and HIV-related mortality, emphasizing the increased susceptibility of this immunocompromised population. The results underscore the need for targeted public health interventions to reduce pollution exposure, particularly for the most at-risk demographic groups. This study contributes to a deeper understanding of environmental health risks faced by individuals living with HIV and informs evidence-based policy recommendations.

Quantifying the impact of COVID-19 restrictions on air pollution in Ahvaz: a comparative dual-approach assessment of observed against baseline and forecasted criteria air pollutants.

In response to the COVID-19 pandemic, the Iranian government swiftly implemented immediate and decisive measures to control the spread of the infection. This study aims to demonstrate the impact of restriction measure on air pollution, also to highlight the potential variability in results that can arias from different methodological approach. A comprehensive dual-approach assessment was conducted to evaluate the effect of the lockdown measures on criteria air pollutants. Firstly, a traditional approach compared air quality during the pandemic period with baseline conditions from 2013 to 2019. Secondly, observed air pollution values during different periods with varying restrictions in 2020 were compared with expected values. This comprehensive analysis allows for a robust comparison and quantification of the impact of different lockdown measures in Ahvaz. The study revealed significant changes in air pollutant concentrations in Ahvaz during 2020, with variations observed across different pollutants. Notable reductions were observed in O3 levels, particularly in November (-54.44% compared to the baseline) and December (-63.58% compared to expected values). Decreases in CO levels were observed in multiple months, while substantial reductions in PM10 and PM2.5 were observed during various periods. Inconsistencies in the magnitudes and directions of changes were found when comparing baseline and forecasted values. The overall stringency index showed an inverse association with changes in O3, NO2, and CO, with international travel controls and restrictions on internal movement having significant impacts. This study provides valuable insights into the impact of COVID-19 lockdown measures on air pollution in Ahvaz, Iran, using a comprehensive dual-approach assessment. The findings highlight the effectiveness of these measures in reducing specific criteria air pollutants and emphasize the importance of implementing appropriate strategies for air quality management during similar public health emergencies.

The complex chemical effects of air pollutants, meteorology and dust on the concentrations of criteria and non-criteria air pollution in Kuwait

Air pollution is the leading environmental health risk factor globally. A number of cities experienced improvements in air quality during the COVID-19 lockdown; however such changes depend on the contributions from natural and anthropogenic sources and meteorological factors pertaining to each city. This study investigated the effects of anthropogenic and meteorological factors on air quality in Kuwait during the COVID-19 lockdown (22 March–10 May 2020) using mixed-effect regression and machine learning models. Criteria (PM2.5, PM10, NO2, O3, SO2, CO) and non-criteria (C6H6, NH3, H2S, NMHC) air pollutants were compared between the lockdown period, and the same period in the preceding and the following years, 2019 and 2021, respectively. The results showed that during the lockdown three criteria air pollutants (PM2.5, NO2, CO) were reduced by 38, 40, 26%, respectively. All no-criteria air pollutants were reduced, except for H2S. The arid region’s harsh desert conditions have major contributions to particulate pollution. Although PM levels are overwhelmed by meteorological and natural dust, stringent abatement measures on oil/gas hydrocarbon and power plant emissions can significantly reduce PM and other pollutants. Abatement measures on traffic pollution can further reduce the NO2 levels. This study highlights Kuwait's ongoing struggle with elevated levels of particulate and gaseous pollution, driven by hydrocarbon emissions and dust. Effectively tackling air quality requires a nuanced understanding of the interplay between air pollutants, meteorological, and dust covariates.

Impact of COVID-19 pandemic on greenhouse gas and criteria air pollutant emissions from the San Pedro Bay Ports and future policy implications

The Ports of Los Angeles and Long Beach, collectively known as the San Pedro Bay Ports, serve as vital gateways for freight movement in the United States. The COVID-19 pandemic and other influencing factors disrupted freight movement and led to unprecedented cargo surge, vessel congestion, and increased air pollution and greenhouse gas emissions from seaport and connected freight system operations beginning in June 2020. In this study, we conducted the first comprehensive monthly assessment of the excess particulate matter, oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions due to the heightened congestion and freight transport activity from ocean-going vessels (OGVs), trucks, locomotives, and cargo handling equipment (CHE) supporting seaport operations. Excess emissions peaked in October 2021 at 23 tons of NOx per day and 2001 tons of CO2 per day. The strategic queuing system implemented in November 2021 significantly reduced the number of anchored and loitering OGVs and their emissions near the ports, even during continued high cargo throughput until Summer 2022. Looking forward, we analyzed projected emissions benefits of adopted California Air Resources Board regulations requiring cleaner and zero-emission trucks, locomotives, and CHE over the next decade. If a repeated port congestion event were to occur in 2035, NOx emissions from land-based freight transport should be lessened by more than 80%. Our study underscores the potential emissions impacts of disruptions to the freight transport network and the critical need to continue reducing its emissions in California and beyond.

How international conflicts and global crises can intertwine and affect the sources and levels of air pollution in urban areas.

This paper analyses the intertwined impacts of the 2018 US sanctions on Iran and the COVID-19 pandemic (as examples of unplanned international conflicts and global crises) on the source and extent of air pollution in Tehran, the capital of Iran. The impacts are parametrized using the levels of criteria air pollutants (CAP) for 5years (2015-2020), which were previously deweathered using the promising machine learning technique of Random Forest (RF). The absolute principal component scores-multiple linear regression (APCS-MLR) method and the bivariate polar plot (BPP) technique are used here to analyze the source apportionment profile of the city for the business as usual (BAU; 2015 to 2018), sanctions (2019), and COVID-19 and sanctions (2020) intervals. The results show the severe impact of the 2018 US sanctions on Tehran's air quality (AQ); O3, NO2, CO, PM2.5, and PM10 levels increased by 117%, 55%, 20%, 35%, and 10%, respectively, while SO2 levels decreased by 30%. The sanctions also triggered a number of events, such as the disruption of the high-grade fuel supply chain and the Mazut crisis, which directly or indirectly accelerated the photochemical production of local tropospheric ozone to some extent. Sanctions also disrupted Tehran's AQ response to the pandemic, with CAP levels decreasing by only 2-7% during the pandemic. The ozone and PM10 BPPs show that the source apportionment profile of the city is dominated by local anthropogenic emission sources, especially urban transport, after the sanctions and the pandemic. Results also show that the impact of soft wars, such as the US sanctions against Iran, on urban air quality degradation is much stronger than that of hard wars, such as the Russia-Ukraine war.

Long-term spatiotemporal variation of benzo[a]pyrene in Japan: Significant decrease in ambient concentrations, human exposure, and health risk

Although Benzo[a]pyrene (BaP) is considered carcinogenic to humans, the health effects of exposure to ambient levels have not been sufficiently investigated. This study estimated the long-term spatiotemporal variation of BaP in Japan over nearly two decades at a fine spatial resolution of 1 km. This study aimed to obtain an accurate spatiotemporal distribution of BaP that can be used in epidemiological studies on the health effects of ambient BaP exposure. The annual BaP concentrations were estimated using an ensemble machine learning approach using various predictors, including the concentrations and emission intensities of the criteria air pollutants, and meteorological, land use, and traffic-related variables. The model performance, evaluated by location-based cross-validation, exhibited satisfactory accuracy (R2 of 0.693). Densely populated areas showed higher BaP levels and greater temporal reduction, whereas BaP levels remained higher in some industrial areas. The population-weighted BaP in 2018 was 0.12 ng m−3, a decrease of approximately 70% from its 2000 value of 0.44 ng m−3, which was also reflected in the estimated excess number of lung cancer incidences. Accordingly, the proportion of BaP exposure below 0.12 ng m−3, which is the BaP concentration associated with an excess lifetime cancer risk of 10−5, reached 67% in 2018. Our estimates can be used in epidemiological studies to assess the health effects of BaP exposure at ambient concentrations.

Combating air pollution significantly reduced air mercury concentrations in China.

In the past decade, China has motivated proactive emission control measures that have successfully reduced emissions of many air pollutants. For atmospheric mercury, which is a globally transported neurotoxin, much less is known about the long-term changes in its concentrations and anthropogenic emissions in China. In this study, over a decade of continuous observations at four Chinese sites show that gaseous elemental mercury (GEM) concentrations continuously increased until the early 2010s, followed by significant declines at rates of 1.8%-6.1% yr-1 until 2022. The GEM decline from 2013 to 2022 (by 38.6%±12.7%) coincided with the decreasing concentrations of criteria air pollutants in China and were larger than those observed elsewhere in the northern hemisphere (5.7%-14.2%). The co-benefits of emission control measures contributed to the reduced anthropogenic Hg emissions and led to the GEM decline in China. We estimated that anthropogenic GEM emissions in China were reduced by 38%-50% (116-151 tons) from 2013 to 2022 using the machine-learning and relationship models.

Machine learning exploring the chemical compositions characteristics and sources of PM2.5 from reduced on-road activity

Particulate nitrate pollution has emerged as a major contributor to haze events in urban environment, due to the rapid increase of vehicle emissions. However, a comprehensive formation mechanisms of PM2.5 responses to vehicle emissions control still remains high uncertainties. In our study, hourly criteria air pollutants, meteorological parameters and chemical compositions of PM2.5 were continuously measured with or without reduced on-road activity at the coastal city in southeast China. XG Boost-SHAP models analysis showed that increasing concentrations of NO3−, NH4+, and BC contribute to elevated PM2.5 levels, due to the influence of vehicle emissions. Based on PMF model results, there was a notable increase in the contributions of traffic-related emissions, industrial activities, and dust sources to PM2.5, with increments of 13%, 4%, and 7%, respectively. In addition, metal elements such as Mn emerged as the primary contributor to hazard quotient (HQ) values, originated from non-exhaust emissions of vehicles, which might cause the potential toxic risks on human health, particularly during haze events. Hence, this study improve the understanding of air quality and human health both direct and indirect responses to vehicle emissions control in future urban management.

Advancements in miniaturized infrared spectroscopic-based volatile organic compound sensors: A systematic review

The global trends of urbanization and industrialization have given rise to critical environmental and air pollution issues that often receive insufficient attention. Among the myriad pollution sources, volatile organic compounds (VOCs) stand out as a primary cluster, posing a significant threat to human society. Addressing VOCs emissions requires an effective mitigation action plan, placing technological development, especially in detection, at the forefront. Photonic sensing technologies rooted in the infrared (IR) light and matter interaction mechanism offer nondestructive, fast-response, sensitive, and selective chemical measurements, making them a promising solution for VOC detection. Recent strides in nanofabrication processes have facilitated the development of miniaturized photonic devices and thus sparked growing interest in the creation of low-cost, highly selective, sensitive, and fast-response IR optical sensors for VOC detection. This review work thus serves a timely need to provide the community a comprehensive understanding of the state of the art in this field and illuminate the path forward in addressing the pressing issue of VOC pollution.

Identifying decadal trends in deweathered concentrations of criteria air pollutants in Canadian urban atmospheres with machine learning approaches

Abstract. This study investigates long-term trends of criteria air pollutants, including NO2, CO, SO2, O3 and PM2.5, and Ox (meaning NO2+O3) measured in 10 Canadian cities during the last 2 to 3 decades. We also investigated associated driving forces in terms of emission reductions, perturbations due to varying weather conditions and large-scale wildfires, as well as changes in O3 sources and sinks. Two machine learning methods, the random forest algorithm and boosted regression trees, were used to extract deweathered mixing ratios (or mass concentrations) of the pollutants. The Mann–Kendall trend test of the deweathered and original annual average concentrations of the pollutants showed that, on the timescale of 20 years or longer, perturbation due to varying weather conditions on the decadal trends of the pollutants are minimal (within ±2 %) in about 70 % of the studied cases, although it might be larger (but at most 16 %) in the remaining cases. NO2, CO and SO2 showed decreasing trends in the last 2 to 3 decades in all the cities except CO in Montréal. O3 showed increasing trends in all the cities except Halifax, mainly due to weakened titration reaction between O3 and NO. Ox, however, showed decreasing trends in all the cities except Victoria, because the increase in O3 is much less than the decrease in NO2. In three of the five eastern Canadian cities, emission reductions dominated the decreasing trends in PM2.5, but no significant trends in PM2.5 were observed in the other two cites. In the five western Canadian cities, increasing or no significant trends in PM2.5 were observed, likely due to unpredictable large-scale wildfires overwhelming or balancing the impacts of emission reductions on PM2.5. In addition, despite improving air quality during the last 2 decades in most cities, an air quality health index of above 10 (representing a very high risk condition) still occasionally occurred after 2010 in western Canadian cities because of the increased large-scale wildfires.

Pathways to zero emissions in California’s heavy-duty transportation sector

California contributes 0.75% of global greenhouse gas (GHG) emissions and has a target of reaching economy-wide net zero emissions by 2045, requiring all sectors to rapidly reduce emissions. Nearly 8% of California’s GHG emissions are from the heavy-duty transportation sector. In this work, we simulate decarbonization strategies for the heavy-duty vehicle (HDV) fleet using detailed fleet turnover and air quality models to track evolution of the fleet, GHG and criteria air pollutant emissions, and resulting air quality and health impacts across sociodemographic groups. We assess the effectiveness of two types of policies: zero emission vehicle sales mandates, and accelerated retirement policies. For policies including early retirements, we estimate the cost of early retirements and the cost-effectiveness of each policy. We find even a policy mandating all HDV sales to be zero emission vehicles by 2025 would not achieve fleetwide zero emissions by 2045. For California to achieve its goal of carbon neutrality, early retirement policies are needed. We find that a combination of early retirement policies and zero emission vehicle sales mandates could reduce cumulative CO2 emissions by up to 64%. Furthermore, we find that decarbonization policies will significantly reduce air pollution-related mortality, and that Black, Latino, and low-income communities will benefit most. We find that policies targeting long-haul heavy-heavy duty trucks would have the greatest benefits and be most cost-effective.

Trend Analysis and Spatial Source Attribution of Surface Ozone in Chaozhou, China

Surface ozone (O3), a critical air pollutant, poses significant challenges in urban environments, as exemplified by the city of Chaozhou in southeastern China. This study employs a novel combination of trend analysis and spatial source attribution techniques to evaluate the long-term dynamics of surface ozone and identify its sources. Utilizing the Kolmogorov–Zurbenko (KZ) filter and percentile regression, we analyzed the temporal trends of daily maximum 8 h moving average ozone (MDA8 O3) concentrations from 2014 to 2023. Our analysis revealed a general long-term downward trend in MDA8 O3 values alongside notable monthly fluctuations, with peak concentrations typically occurring in October and April. Additionally, the percentile regression analysis demonstrated a significant downward trend in MDA8 O3 concentrations across nearly all percentiles, with larger decline rates at higher percentiles, highlighting the effectiveness of local and regional O3 management strategies in Chaozhou. The changes in MDA8 O3 concentrations were mainly influenced by the short-term component, contributing 62.2%, while the contribution of the long-term fraction is relatively small. This suggests a significant influence of immediate meteorological conditions and transient pollution events on local O3 levels. To further elucidate the origins of high O3 concentrations, trajectory cluster analysis, trajectory sector analysis (TSA), and potential source contribution function (PSCF) analysis were conducted. The trajectory cluster analysis revealed that the northeast air mass was the main transport air mass in Chaozhou during the study period, accounting for 39.1% of occurrences. The northeast cluster C with medium-distance trajectories corresponds to higher concentration of O3, which may be the main transport pathway of O3 pollution in Chaozhou. TSA corroborates these findings, with northeast sectors 1, 2, and 3 accounting for 50.3% of trajectory residence time and contributing 52.2% to O₃ levels in Chaozhou. PSCF results further indicate potential high O₃ sources from the northeast, especially in autumn. This comprehensive analysis suggests that Chaozhou’s elevated O3 levels are influenced by both regional transport from the northeast and local emissions. These findings offer crucial insights into the temporal dynamics of surface O3 in Chaozhou, paving the way for more effective and targeted air quality management strategies.

Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (β: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (β:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Optimizing NO2 monitoring network using a background map for spatial heterogeneity stratification

Nitrogen dioxide (NO2) is a critical air pollutant affecting health and the environment. However, existing monitoring station networks often fail to adequately capture the regional distribution of NO2, indicating a need for enhanced sampling strategies. This study focuses on Southwest Fujian in China and introduces a high-precision NO2 background map to delineate spatial stratified heterogeneity. Subsequently, the Mean of Surface with Non-homogeneity (MSN) method was employed to optimize the design of the NO2 monitoring network, proposing the addition of 125 new stations. The Bayesian Kriging analysis, utilized to evaluate the optimized network, resulted in a coefficient of determination (R2) of 0.87, a mean absolute percentage error (MAPE) of 9.61%, a mean absolute error (MAE) of 2.75 μg/m3 and a root mean square error (RMSE) of 2.47 μg/m3. The improved accuracy and efficiency of the NO2 monitoring network were validated against the background map. This research underscores the effectiveness of integrating precise mapping techniques with strategic network optimization for superior environmental monitoring outcomes.

A relationship between urban green space and PM2.5 levels in urban and suburban areas of Bangkok

Abstract Bangkok, Thailand's densely populated capital, faces critical air pollution due to overpopulation, traffic, industry, and biomass burning. Of particular concern is the escalating PM2.5 pollution, which poses significant risks to both public health and the environment. This research investigates the relationship between Bangkok's urban green spaces (UGS) and PM2.5 levels by comparing the concentrations of PM2.5 in non-green areas (on roads) and green areas (city parks) in both urban (Ratchathewi) and suburban (Lat Krabang) locations. The methodology in this study involves data collection from secondary sources and statistical analysis using ANOVA, T-tests, and the Mann-Kendall test with Sen's slope. The findings indicate a consistent expansion of UGS within the study areas, while levels of air pollution exhibit discernible seasonal fluctuations. Notably, concentrations of PM2.5 near roads were significantly higher than those of nearby parks, with 43% and 38% higher levels at Ratchathewi and Lat Krabang, respectively. Several factors contribute to this discrepancy, encompassing the proximity to pollution sources, the processes of dispersion and dilution, the presence of vegetation and green spaces, and the role of physical barriers. Based on the research findings, recommendations propose to improve air quality through UGS initiatives and collaboration with relevant stakeholders.

Statistical analysis and environmental impact of pre-existing particle growth events in a Northern Chinese coastal megacity: A 725-day study in 2010–2018

Pre-existing particles usually constitute the major fraction of atmospheric particles, except during some episodes in the presence of strong emissions and/or secondary generation of fresh particles. Previous case studies have investigated the growth of pre-existing particles and their potential environmental and climate impacts. However, there is limited knowledge about the statistical characteristics of these growth events and related effects. In this study, we examine pre-existing particle growth events using a large dataset (725 days from 2010 to 2018) collected at a coastal megacity in northern China. The occurrence frequency of pre-existing particle growth events was 12.4 % (90 out of 725 days). When these events were related to measured criteria air pollutants, no significant differences were found in PM2.5, SO2, NO2 and NO2 + O3 concentrations between periods with and without pre-existing particle growth events. These 90-day events can be further classified into two categories, i.e., Category 1, with 68 % of events representing the growth of pre-existing particles alone, and Category 2, with 32 % of events representing the simultaneous growth of pre-existing and newly formed particles. In Category 2, the growth rates of pre-existing particles and newly formed particles were close in 21 % of the cases, while pre-existing particles exhibited significantly larger growth rates in 69 % of the cases. Conversely, in 10 % of the cases, the growth rates of newly formed particles were larger. The different growth rate mechanisms were discussed in terms of the volatility of atmospheric condensation vapors. In addition, we present case studies on the impact of pre-existing particle growth on cloud condensation nuclei simultaneously measured, specifically considering the chemistry of condensation vapors and pre-existing particles.

An integrated uncrewed aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Abstract. In this study, an uncrewed aerial vehicle (UAV) platform with sensing and sampling systems was developed for three-dimensional (3D) measurements of air pollutant concentrations. The sensing system of this platform contains multiple microsensors and Internet of Things devices for determining the 3D distributions of four critical air pollutants and two meteorological parameters in real time. Moreover, the sampling system comprises remote-controllable gas sampling kits, each of which contains a 1 L Tedlar bag for the 3D measurement of volatile organic compound (VOC) concentrations according to the Toxic Organics-15 (TO-15) method of the US Environmental Protection Agency. The performance of the developed UAV platform was verified in experiments where it was used to detect air pollutant emissions from a large industrial zone in Taiwan that included a traditional industrial park, a precision machinery park, and a municipal waste incineration plant. Three locations were selected as field measurement sites according to the prevailing local wind direction. The vertical distributions of four critical air pollutants, the ambient temperature, and the relative humidity were determined from data gathered at the aforementioned sites in March and May 2023. A total of 56 and 72 chemical species were qualitatively and quantitatively analyzed in these two periods, respectively. The experimental results verified the feasibility of using the proposed UAV platform for accurately evaluating the air pollutant concentration distribution and transport in an industrial zone. The sampling system can be used as the sampling part of the TO-15 method, thus extending the method to measure the 3D distribution of VOCs in an area. The UAV platform can serve as a useful tool in the management of and decision-making process for air pollution in industrial areas.

The Spatial–Temporal Emission of Air Pollutants from Biomass Burning during Haze Episodes in Northern Thailand

Air pollutants from biomass burning, including forest fires and agricultural trash burning, have contributed significantly to the pollution of the Asian atmosphere. Burned area estimates are variable, making it difficult to measure these emissions. Improving emission quantification of these critical air pollution sources requires refining methods and collecting thorough data. This study estimates air pollutants from biomass burning, including PMs, NOX, SO2, BC, and OC. Machine learning (ML) with the Random Forest (RF) method was used to assess burned areas in Google Earth Engine. Forest emissions were highest in the upper north and peaked in March and April 2019. Air pollutants from agricultural waste residue were found in the lower north, but harvesting seasons made timing less reliable. Biomass burning was compared to the MODIS aerosol optical depth (AOD) and Sentinel-5P air pollutants, with all comparisons made by the Pollution Control Department (PCD) Thailand air monitoring stations. Agro-industries, mainly sugar factories, produce air pollutants by burning bagasse as biomass fuel. Meanwhile, the emission inventory of agricultural operations in northern Thailand, including that of agro-industry and forest fires, was found to have a good relationship with the monthly average levels of ambient air pollutants. Overall, the information uncovered in this study is vital for air quality control and mitigation in northern Thailand and elsewhere.