Air Pollution Variables Research Articles

Understanding the variability of ground-level ozone and fine particulate matter over the Tibetan plateau with data-driven approach

The Tibetan Plateau, known as the “Third Pole”, is susceptible to ground-level ozone (O3) and fine particulate matter (PM2.5) pollution due to its unique high-altitude environment. This study constructed random forest regression models using multi-source data from ground measurements and meteorological satellites to predict variations in ground-level O3 and PM2.5 concentrations and their influencing factors across seven major cities in the Tibetan Plateau over two-year periods. The models successfully reproduced O3 and PM2.5 levels with satisfactory R-squared values of 0.71 and 0.73, respectively. Results reveal combustion-related carbon monoxide (CO) and nitrogen dioxide (NO2) as the most substantial influences on O3 and PM2.5 concentrations. Solar radiation, geographical factors, and meteorological variables also played crucial roles in driving pollutant variations. Conversely, transport-related and human activity factors exhibited relatively lower significance. High O3 and PM2.5 pollution occurred during pre-monsoon and post-monsoon/winter seasons, driven by solar radiation and emissions, respectively. While CO consistently contributed across cities and seasons, key influencing factors varied locally. This study unveils the key driving forces governing air pollutant variations across the Tibetan Plateau, shedding light on complex atmospheric processes in this unique high-altitude region.

Time series analysis of the interaction between ambient temperature and air pollution on hospitalizations for AECOPD in Ganzhou, China

This study aimed to investigate the univariate and bivariate effects of ambient temperature and air pollutants on 57,251 inpatients with AECOPD (Acute Exacerbation of Chronic Obstructive Pulmonary Disease) in Ganzhou from January 1, 2016, to December 31, 2019. We categorized the daily mean temperature and air pollutant variables based on the exposure–response curve of the Distributed Lag Non-Linear Model. Poisson regression model was used for interaction and stratification analysis. The Relative Excess Risk due to Interaction (RERI) with 95% confidence intervals (95% CI) between daily mean temperature (Tmean) and air pollutants including NO2, PM2.5, and PM10 were − 0.428 (95% CI − 0.637, − 0.218), -− 0.227 (95% CI − 0.293, − 0.161), and − 0.119 (95% CI − 0.159, − 0.079). Further stratification analysis showed the relative risk (RR) (95% CI) of high NO2 (> 33 μg/m3) at low Tmean (≤ 28 °C) was 1.119 (95% CI 1.096, 1.142). Low temperatures with high PM10 in men and high PM2.5 in women were associated with a higher risk of AECOPD hospitalization. The results indicate a higher risk of hospitalization for AECOPD when there is with high concentrations of air pollution at low temperatures.

Trends of air pollution variations during pre-Diwali, Diwali and post-Diwali periods and health risk assessment using HAQI in India

During 2019 to 2022, a comprehensive study was conducted across 71 stations spanning over 15 states in India to investigate air quality variations during the pre-Diwali, Diwali, and post-Diwali periods. The investigation, centred on the extensive use of firecrackers during festive times, revealed a short-term deterioration in air quality with long-lasting impacts. PM2.5 and PM10 levels were found to significantly increase during Diwali and the post-Diwali period compared to the pre-Diwali period. The levels of PM2.5 and PM10 experience respective increases of 44.66% and 11.03%, 29.12% and 05.41%, 52.63% and 23.38%, and 39.65% and 17.61% during Diwali period from 2019 to 2022, where 68.84% and 34.32%, 44.42% and 29.42%, and 48.70% and 25.22% in 2019, 2021 and 2022 in post-Diwali period compared to the pre-Diwali. The Mann–Kendall and Sen's slope analyses reveal statistically significant positive trends (increasing) in both PM10 and PM2.5 levels during the Diwali festival period. Notably, the study introduced the Health Hazard-based Air Quality Index (HAQI), which demonstrated higher values than the National Ambient Air Quality Index (NAAQI). Unlike NAAQI, which considers individual pollutant sub-index values, and considered highest one of sub-index values in AQI analysis. HAQI provides a more comprehensive assessment of health risks associated with exposure to multiple pollutants effects. In 2022, AQI and HAQI values during pre-Diwali were 114 and 146.18, during Diwali were 173 and 332.18, and post-Diwali were 191 and 414.14, respectively. These findings underscore the elevated risk during Diwali and the post-Diwali period due to heightened pollution levels, emphasizing the need for public awareness and potential mitigation strategies. The study calls attention to the importance of considering cumulative health impacts and suggests the adoption of HAQI for more informative air quality assessments.

Air Quality Monitoring and the Safety of Farmworkers in Wildfire Mandatory Evacuation Zones.

The increasing frequency and severity of wildfires due to climate change pose health risks to migrant farm workers laboring in wildfire-prone regions. This study focuses on Sonoma County, California, investigating the effectiveness of air monitoring and safety protections for farmworkers. The analysis employs AirNow and PurpleAir PM2.5 data acquired during the 2020 wildfire season, comparing spatial variability in air pollution. Results show significant differences between the single Sonoma County AirNow station data and the PurpleAir data in the regions directly impacted by wildfire smoke. Three distinct wildfire pollution episodes with elevated PM2.5 levels are identified to examine the regional variations. This study also examines the system used to exempt farmworkers from wildfire mandatory evacuation orders, finding incomplete information, ad hoc decision-making, and scant enforcement. In response, we make policy recommendations that include stricter requirements for employers, real-time air quality monitoring, post-exposure health screenings, and hazard pay. Our findings underscore the need for significant consideration of localized air quality readings and the importance of equitable disaster policies for protecting the health of farmworkers (particularly those who are undocumented migrants) in the face of escalating wildfire risks.

Satellite-Based Mapping for Seasonal Variations of Air Pollution and its Environmental Effects in Odisha

Satellite-Based Mapping for Seasonal Variations of Air Pollution and its Environmental Effects in Odisha

Diurnal variation of indoor air pollutants and their influencing factors in educational buildings: A case study using LASSO-based ANNs

This study explores the diurnal variations and influencing factors of PM2.5, NO2, and ozone concentrations in educational buildings. Utilizing an integrated system of indoor and outdoor sensors, building automation control networks, and walk-through inspections, air quality data along with relevant characteristics were collected from ten educational buildings in Central Florida. Advanced Neural Network models (RNNs and CNNs), including the Long Short-Term Memory (LSTM) and the Attention Temporal Convolutional Network (ATCN) algorithms based on the Least Absolute Shrinkage and Selection Operator (LASSO), were developed to accurately identify diurnal patterns in indoor air quality (IAQ) and the differences in influencing factors. The findings indicate greater variability in diurnal differences and factors influencing indoor NO2 and ozone concentrations compared to PM2.5. Although the factors influencing day and night PM2.5 levels were similar, there were significant differences in the contribution weights of these factors. Optimized RNNs and CNNs significantly outperformed standard Artificial Neural Network (ANN) models in dynamically simulating and predicting target pollutants. Comparative analysis of the root-mean-square error (RMSE) demonstrated that LASSO-LSTM models comprehensively outperformed LASSO-ATCN models by averaging 13.4% (p < 0.05). These results can be referenced in studies concerning Indoor Air Quality (IAQ) control conducted in similar environmental settings.

The Variability of Indoor Air Pollutants in the Office and Their Impact on the Workers’ Health

The Variability of Indoor Air Pollutants in the Office and Their Impact on the Workers’ Health

Characterization of Spatial and Temporal Variations in Air Pollutants and Identification of Health Risks in Xi’an, a Heavily Polluted City in China

The study of the temporal and spatial characteristics of air pollutants in heavily polluted cities is extremely important for analyzing the causes of pollution and achieving a viable means of control. Such characteristics in the case of Xi’an, a typical heavily polluted city in Fenwei Plain, China, have remained unclear due to limitations in data accuracy and research methods. The monthly, daily, and hourly patterns of O3 and particulate matter (PM2.5 and PM10) are analyzed in this study using on-site data provided by an urban air quality monitoring network. The analysis of variance (ANOVA) method was used to compare differences in pollutant concentrations during different seasons and time periods. The spatial distributions of O3, PM2.5, and PM10 at different time points following interpolation of the air quality monitoring sites have been analyzed. The results show that the O3 concentration from 12 p.m. to 3 p.m. was significantly higher than that in the morning and evening, and the concentrations of PM2.5 and PM10 from 7 p.m. to 10 p.m. were significantly higher than those in the morning and afternoon. The number of qualified days for PM2.5 was less than 30 and unqualified days for O3 was more than 100 in 2019. There is a potential risk of exposure to pollution with associated health risks. Even on the same day, the spatial pollutant distributions at different time points can differ significantly. This study provides a scientific basis for reducing O3 and particulate matter exposure. Outdoor activities in the morning in summer are more beneficial to reduce O3 exposure, and outdoor activities should be curtailed in the evening in winter to reduce particulate exposure. This study provides a scientific basis for the government to formulate public health policies to reduce pollution exposure from outdoor activities.

Assessment of spatial variation in lung cancer incidence and air pollutants: spatial regression modeling approach

ABSTRACT A notable finding is that Kerala’s capital Thiruvananthapuram has shown an increasing trend in lung cancer (LC) incidence. Long-term exposure to air pollution is a significant environmental risk factor for LC. This study investigated the spatial association between LC and exposure to air pollutants in Thiruvananthapuram, using Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The results showed that overall LC incidence rate was 111 per 105 males (age >60 years), whereas spatial distribution map revealed that 48% of the area had an incidence rate greater than 150. The results revealed a significant association between PM2.5 and LC. SLM was identified as the best model that predicted 62% variation in LC. GWR model improved model performance and made better local predictions in the southeastern parts of the study area. This study explores the effectiveness of spatial regression techniques for dealing spatial effects and pinpointing high-risk areas.

Air Pollutant Spatiotemporal Evolution Characteristics and Effects on Human Health in Xi'an City

Xi'an is the political, economic, and cultural center of northwest China with a developed industry. Air pollution incidents have brought great challenges to the high-quality development of the social economy. It is vital to study air pollution characteristics and clarify their impact on human health. In this study, we first analyzed the spatiotemporal variations in air pollutants in the study region from 2015 to 2021. Then, the air quality index (AQI), aggregate air quality index (AAQI), and health risk-based air quality index (HAQI) were used to assess health risks. Based on these, the AirQ2.2.3 model was used to quantify health effects. The results showed that the major pollutants were PM10, PM2.5, and O3. The main pollution characteristics of the study area were terrain characteristics and the mixed pollution of anthropogenic emissions. Compared to that of AQI, AAQI and HAQI showed better classification performance for pollution levels. HAQI revealed that approximately 80 % of the population was exposed to unhealthy air throughout the year in the study region. People were most exposed to unhealthy air in winter, followed by autumn and spring, and the least in summer. The AirQ2.2.3 model quantified the total mortality proportions attributable to PM2.5, PM10, SO2, CO, NO2, and O3, which were 0.99 %, 2.04 %, 0.41 %, 1.72 %, 8.76 %, and 3.67 %, respectively. The attributable proportion of mortality of the respiratory system and cardiovascular diseases was consistent with the change rule of total mortality.

Concentration and variation of traffic-related air pollution as measured by carbon monoxide in Hawassa City, Ethiopia

Air pollution is a pressing problem and causes millions of deaths each year related to its effects on respiratory health worldwide. Despite its significant impact, information on air pollution in many cities, including Hawassa city, remains limited. The aim of this study was to determine the concentrations and variations of traffic-related air pollution, as measured by carbon monoxide, and its relationship with temperature, relative humidity, traffic flow, and road routing in Hawassa City. We monitored carbon monoxide (CO) concentrations in Hawassa city, Ethiopia, for 24 days using real-time monitors with carbon monoxide sensor head. A total of 24 different roads (12 high-traffic congested roads and 12 low-traffic congested roads) were included as monitoring sites. We conducted 1 hour monitoring at each monitoring site in the morning and afternoon to characterize the temporal variations. Accordingly, the average carbon monoxide concentrations varied temporally at different times. In addition, the average carbon monoxide concentrations on paved main roads, traffic light roads and low-traffic flow roads were 4.87 ± 0.6 ppm, 5.38 ± 0.8 ppm and 1.62 ± 0.53 ppm, respectively. The study also identified factors that correlated positively with carbon monoxide concentration, including temperature, relative humidity, traffic flow and road routing. The study concludes that long-term monitoring of carbon monoxide concentrations is necessary to fully characterize the health effects. The study also provides valuable insights for urban planners when implementing measures to mitigate the negative effects.

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands.

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016–2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50–0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24–0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays’. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35–0.70; PM2.5 hourly R2 = 0.01–0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

Spatial Temporal Analysis of Air Quality and their Relation to Meteorological Parameters, in India

Air pollution has become a significant global issue, with India ranking 8th in the world for hazardous air quality based on the latest 2022 report. (World Air Quality Report 2023). The growing urban sprawl and human-induced environmental activities have led to a decline in air quality. The Air Quality Index assesses the level of pollution in the air at a particular region. It is a fundamental right for individuals to be informed about the quality of the air they breathe for their well-being. This study aims to examine air pollution levels and trends across various geographical locations in India using data provided by the Central Pollution Control Board under the Ministry of Environment, Forest, and Climate Change in India. The study focuses on analyzing the annual growth of pollutants such as SO2, NO2, PM10, and PM2.5 from 2016 to 2021, collected from monitoring stations in 28 states across 256 locations. The research evaluates the impact of different particulate matter concentrations and showcases the spatial variation of air pollutants between 2016 and 2021. Results indicate a significant decrease in air pollution levels during lockdown periods compared to pre-lockdown periods. On an average (over all cities), more than 24% decrease has been observed for all the AQI of the pollutants. Additionally, a comparative analysis reveals correlations between meteorological factors and air quality, showing that temperature, relative humidity, and wind speed are negatively correlated with AQI, while surface pressure is positively correlated. Temporal analysis was also conducted to examine the relationship between air pollutant concentrations and meteorological parameters.

Seasonal Variation in Short-Term Ambient Air Pollutants and ST-Elevation Myocardial Infarction Admissions: An Innovative Exploration of Air Pollution’s Health Consequences

Cardiovascular diseases (CVDs) persist as a significant contributor to global morbidity and mortality despite advances in medical technology. Air pollution has emerged as a significant contemporary challenge due to increased energy consumption and rapid economic development. The study utilized multivariable Poisson regression and Distributed Lag Models (DLM) to assess the link between brief exposure to outdoor air pollutants (PM10—particulate matter with a diameter ≤ 10 μm, NO2—nitrogen dioxide, and O3—ozone) and the risk of acute myocardial infarction with ST-segment elevation (STEMI) hospitalization, stratified by season. The research was conducted from January 2019 to December 2021 at the University Hospital in Timisoara, Romania, and daily records were collected for STEMI admissions, atmospheric pollutant levels, and meteorological parameters. The most pronounced impacts were observed with each 10 μg/m3 increase at lag 07 for PM10 during summer, leading to a 2% increase in STEMI admissions, and for NO2 during spring at lag 07, resulting in a 0.9% rise in CVD incidence. Men, middle-aged adults, and older adults exhibited greater susceptibility to elevated NO2 and PM10 concentrations than women and younger individuals. Brief exposure to diverse air pollutants heightens the likelihood of hospitalization due to STEMI, particularly among men and adults over 45. Effective measures must be implemented to mitigate these impacts, especially for vulnerable populations.

Impact of Urbanization on Air Quality of Dehradun District

The increasing trend of urbanization poses a significant challenge to environmental sustainability, particularly concerning air quality. This study examines the impact of rapid urbanization on the Dehradun district land use and land cover (LULC), with the built-up area increasing from 44.2 km² to 276.5 km² between 2000-2019. The LULC analysis highlights a significant expansion of built-up areas influencing regional air quality. A comprehensive analysis of air pollutants and environmental factors spanning 2000 to 2019, utilizing TROPOMI, OMI, MODIS, MOPITT, and CHIRPS data, was also conducted. Spatial variations of air pollutants, including CO i.e. carbon monoxide, HCHO i.e. formaldehyde and NO2 i.e. nitrogen dioxide demonstrate higher concentrations in urbanized areas. Temporal variations exhibit seasonal peaks during the summer months. A 15-year trend analysis indicates a consistent rise in NO2 and HCHO, attributed to urbanization and anthropogenic activities. The correlation matrix highlights strong positive correlations among air pollutants, suggesting common sources or influences. The correlation between NO2 and CO stands at 0.652, between HCHO and CO is 0.583, and between HCHO and NO2 is 0.619. Higher correlation values of more than 0.4 are observed between the land surface temperature and pollutant concentrations, highlighting the influence of anthropogenic activities on temperature rise. In an attempt to mitigate the negative effects of uncontrolled urban sprawl on the district of Dehradun's air quality and overall environmental health, the study underscores the pressing need for sustainable urban planning. These findings contribute valuable insights for decision-makers to formulate effective strategies for environmental conservation against the unfavourable consequences of swift urbanization.

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

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

Variability and health impact of air pollutants and bioaerosols in multi-functional indoor environments with mechanical ventilation system

The condition of indoor environments significantly impacts human daily life and health, where exposure to harmful substances affects the resilience of occupants. To understand the dynamics of indoor air quality and its implications for occupant health thus this study evaluated air pollutant concentration (TVOC, NO2, O3, CO, CO2, PM10, PM2.5, and PM1) and bioaerosol in mechanically ventilated offices, laboratories, corridors, a library and a mechanical workshop. Simultaneous measurements of all air pollutants were performed for 24 h, including non-working hours, while bioaerosol was sampled for fungal identification and quantified via the colony counting method. High concentrations of PM10, PM2.5, PM1, O3 and bioaerosol were found in the mechanical workshop, where these pollutants were strongly related to humidity (p < 0.05) and indoor source contributions. Other air pollutants such as TVOC had the highest concentrations in the office areas during working hours with an average concentration of 361 ± 14.8 μg m−3, while for CO, the highest concentration was observed in the laboratories for both working hours (442 ± 26.0 ppb) and 24 h average concentration (438 ± 22.1 ppb). Dominant species identified in bioaerosol were Aspergillus sp., Geotrichum sp., Fusarium sp., and Penicillium sp., where the exposure to bioaerosol was found to be of low risk to all workers as the hazard quotient (HQ) was <1 for all multi-functional indoor environments. The results from this study suggest that indoor air pollutants and bioaerosol concentrations have a variety of characteristics and source contributions, especially from human activities and indoor sources.

Diurnal variation of air pollutants and their relationship with land surface temperature in Bengaluru and Hyderabad cities of India

Air pollution and Land Surface Temperature (LST) are crucial components of urban environments. As urbanization and industrialization increase, the two components affect the natural environment of the urban areas. The present study investigated the daytime and nighttime spatiotemporal distribution of air pollutants and examined their relationship with LST for Bengaluru and Hyderabad cities of India. Daytime/nighttime/seasonal/spatial variations of air pollutants and LST, sensitivity of air pollutant in affecting LST, relationship between air pollutants and LST, and hotspots/coldspots analysis for LST and air pollutants was carried out. The study utilized, air pollutants data of 5-years from air quality monitoring stations and LST data of overlapping period from MODIS satellite. PM2.5 exceeded the national and international air quality standards while all other parameters were within specified limits/guideline values, at both the cities. The study found that LST had maximum sensitivity towards PM2.5 and NO2 for both the cities indicating that both these parameters are more effective in causing changes in the LST values. High Urban Thermal Field Variance Index (UTFVI) values for urban areas demonstrated strong surface urban heat island (SUHI) during nighttime in both the cities. Pearson's correlation analysis showed that LST, at Hyderabad, was positively correlated with PM2.5 (r = 0.714) during daytime. Relationship of LST with all air pollutants and meteorological parameters was investigated simultaneously and very strong relationship of LST with all other parameters was observed for both daytime and nighttime for both the cities. The R2 values at Bengaluru were 0.98 and 0.92 for nighttime and daytime, respectively, while the corresponding values were 0.87 and 0.93, respectively for Hyderabad. Hotspot analysis showed that total area of hotspots/coldspots undergoes temporal variations and the corresponding area during daytime was higher/lower compared to nighttime for different parameters. The trend of increase/decrease of daytime area of hotspots/coldspots, over nighttime area, was also different from different parameters as well as it was observed different for both the cities. Overall, the present study provides holistic insights into the interaction between air pollutants and LST in urban contexts. By understating these interactions and variations it helps urban planning and policymaking mitigate the challenge of the rise in LST and air pollutants.

Deseasonalized trend of ground-level ozone and its precursors in an industrial city Kaohsiung, Taiwan

Mitigating ground-level ozone (GLO) remains challenging due to its highly nonlinear formation process. Thus, understanding GLO pollution trends is crucial for developing effective control strategies, especially Kaohsiung industrial city, Taiwan. Based on the long-term monitoring data set of 2011–2022, temporal analysis reveals that monthly mean GLO peaks in autumn (40.66 ± 5.10 ppb), carbon monoxide (CO) and major precursors such as nitrogen oxides (NOx), nonmethane hydrocarbons (NMHC) reach their highest levels in winter. The distinct seasonal variation of air pollutants in Kaohsiung is primarily influenced by the unique blocking effect of the mountainous area under the northeasterly wind, as the city is situated downwind, causing high GLO levels during autumn due to the accumulation of stagnant air hindering the dispersion of pollutants. Over the 12 years (2011–2022), the deseasonalized trend analysis was conducted with p < 0.001, revealing a stabilization trend of GLO (+0.04 ppb/yr) from a previous sharp increase. The observed improvement is credited to a drastic decrease in total oxidants (Ox) at −0.63 ppb/yr due to significantly reducing their precursors. Furthermore, the effectiveness of precursor reduction is also supported by GLO daily maximum profile changes. While high GLO events (>120 ppb) decrease, days within midrange (60–80 ppb) rise from 24.4% to 33.3%. A notable difference emerges when comparing daytime and nighttime GLO. While daytime GLO decreased at −0.22 ppb/yr, nighttime GLO increased at +0.34 ppb/yr. Weakened nocturnal titration effects accounted for the nighttime increase. The distinct spatial variations in GLO trends on a citywide scale underscore that areas with complicated industrial activities may not benefit from a continuing reduction of precursors compared to less-polluted areas. The findings of this study hold significant implications for improving GLO control strategies in heavily industrialized city and provide valuable information to the general public about the current state of GLO pollution.

3-D AIR POLLUTION ESTIMATION USING A HYBRID SPATIAL MODEL: A CASE STUDY OF ZHUNAN-MIAOLI AREA, TAIWAN

Abstract. The rapid global urbanization has transformed cityscapes, giving rise to iconic skyscrapers that define modern cities. However, alongside this urban evolution, a pressing concern arises –the air quality within these towering urban environments. Fine particulate matter, known as PM2.5, poses a grave threat to human health and the environment. These tiny particles, measuring 2.5 micrometers or less, can penetrate deep into the human respiratory system, posing severe health risks. Due to the limitations of traditional land-use regression models in estimating the variation of air pollution with altitude, this study employs a novel hybrid spatial model to assess the three-dimensional distribution of PM2.5 in the atmosphere.We employ a comprehensive methodology, integrating diverse datasets and advanced modelling techniques, to uncover significant findings. Our analysis reveals the non-uniform nature of PM2.5 distribution, both horizontally and vertically. Variable selection identifies key factors influencing PM2.5 levels, including Broadleaf Forest, Carbon Monoxide (CO), and Height. Our ensemble model demonstrates robust performance, with Gradient Boosting Regression (GBR) and Random Forest Regression (RFR) exhibiting superior predictive capabilities. This study provides valuable insights into the complex interplay of environmental factors affecting PM2.5 concentrations in high-rise urban environments, emphasizing the need for targeted air quality management strategies considering both horizontal and vertical variations.