Air Pollution Trends Research Articles

SMOG CRISIS IN LAHORE: EVALUATING AIR QUALITY TRENDS AND PUBLIC HEALTH IMPLICATIONS

Seasonal smog renders Lahore, Pakistan, an environment legally with low-quality air and poor health for frequent cases. The smog includes such air pollutants as particulate matter, including PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), which worsens respiratory and cardiovascular diseases. Air pollution trends in pre-smog season and during smog season and the related public health consequences of this phenomenon are examined in this research. The study compared the values of air quality parameters, as well as health data on the whole population in Lahore, in the period before smog and the period of maximum smog intensity. The participants consisted of 500 persons, and data about the air quality indicators, including PM2.5, PM10, CO, NO2, and SO2, was obtained. Imagery indices, self-reported symptoms, hospitalization, and cardiovascular events were examined. Hypothesis testing analyzing the perception of air quality and confirmed behavioral change were concerned with a survey regarding the usage of masks, awareness of air quality alerts, and frequency of outdoor trips. The data were analyzed and compared using t-tests with a test of significance set at 0.05 for all the tests done in this research. The results of the present study reveal that air quality parameters such as PM2.5, PM10, CO, NO2, and SO2 had enhanced values during the smog-burst period as compared to the pre-smog condition. The PM2.5 concentrations were raised from 60.52 µg/m³ to 180.34 µg/m³, and those of PM10 from 85.24 µg/m³ to 230.14 µg/m³. Other effects included improvements and worsening health indicators for persons suffering various ailments during peak smog. Hospital admission per 1000 people rose from 20.42±5.83 to 45.79±10.10 respiratory symptoms rose from 3.23±1.17 to 7.84±1.90 and cardiovascular incidents rose from 1.19±0.41 to 3.52±1.27. The number of asthma, bronchitis, and COPD cases also rose during the peak smog period. Asthma increased from 12.52% to 28.74%, bronchitis from 10.10% to 22.30%, and COPD from 4.93% to 12.12%. Knowledge about air quality alerts and wearing a mask during the smog season also changed respectively. The result demonstrated that Lahore recorded the maximum AQI and pollutants compared to other big cities in Pakistan such as Karachi, Islamabad, and Faisalabad. The findings presented in the paper show an alarming increase in air pollution and adverse effects on human health particularly during the period of smog in Lahore. People’s knowledge and changes in their behavior, including mask-wearing and reduced time spent outdoors, indicate that new and efficient preventive measures in public health and the environment should be developed to combat smog impacts on human health.

Clearing the Air to Address Pollution's Cardiovascular Health Crisis.

Air pollution is a critical global health issue that significantly impacts cardiovascular health. The air pollutant PM2.5 (particulate matter with a diameter of 2.5 micrometres or less) has been positioned as a leading environmental risk factor for morbidity and mortality, especially from cardiovascular diseases (CVDs). Using data from the World Health Organization (WHO), Global Health Observatory, and the United Nations Environment Programme, we explored global trends in air pollution, with a focus on PM2.5 levels, the implications for cardiovascular health, and the policy measures aimed at reducing their impact. Despite progress in reducing pollution levels in high-income countries, global trends show a limited annual reduction in PM2.5 concentration. The analysis highlights disparities between regions, with low- and middle-income countries bearing the brunt of air pollution-related CVDs. In 2019 alone, ambient air pollution was responsible for approximately 4.2 million deaths worldwide. Of these, 70% were caused by CVDs, with approximately 1.9 million deaths from ischemic heart disease and 900,000 deaths from stroke. Policy gaps remain a challenge, with many countries lacking adequate legally binding air quality standards. We recommend the adoption of WHO air quality guidelines, enhanced monitoring of air pollution levels, and increased investment in interdisciplinary research to understand the full scope of air pollution's effects on cardiovascular health. Addressing the global cardiovascular crisis linked to air pollution will require coordinated efforts from policymakers, healthcare systems, and global health organisations.

Assessing the impact of meteorological factors and air pollution on respiratory disease mortality rates: a random forest model analysis (2017-2021).

Air pollution poses a significant threat to the health of all living beings on our planet. It has been scientifically established as a crucial factor affecting mortality rates, respiratory illnesses, mental well-being, and overall health. This study aimed to investigate the impact of air pollution and meteorological factors on respiratory disease mortality rates in Mashhad in 2017-2021 using a Random Forest (RF) model. At first, the daily statistics of meteorological parameters (pressure, humidity, temperature, solar radiation) during 2017-2021 were collected. The information related to pollutants pollutants such as PM2.5 (which is defined as particulate matter with less than 2.5 micrometer diameter and potentially harmful to humans), PM10 (Particles with a diameter of 10 micrometers or less that can negatively impact both human health and environmental conditions.), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) was collected. the mortality statistics from respiratory diseases were collected from the Health system registaration (Sina). Then we used the RF regression model in Excel and Python software to investigate the interaction between the mentioned variables. The escalating trend of air pollution in Mashhad has led to an expected increase in respiratory-related hospitalizations. This necessitates urgent air pollution control measures. Concurrently, the study of pollutants and climatic elements, as substantiated by global epidemiological studies, is crucial. In Mashhad, the second most polluted city in Iran, climatic factors like humidity, sunshine duration, temperature, pressure, and sunlight intensity exacerbate atmospheric pollution levels, impacting human health and ecosystems. The R2, RSME, and MAE of RF model are 0.73, 2.52, and 2 which indicate that the model has successfully identified the relationship between input variables and the target variable, and it will exhibit high accuracy in predictions. In this study, the most influential factor was identified when the Variance Inflation (VI) factor reached a value of 0.548. This indicates a strong correlation between this factor and the death rate of patients during the specified period. Furthermore, we analyzed by excluding the day and month plans from our model. The results showed that the factor with the highest coefficient in the executive model was related to pressure, with a VI value of 0.049. This suggests that pressure plays a significant role in our model and has a substantial effect on the death rate of patients. In the study of various pollutants, it was found that PM10 had the most significant impact on the mortality rate of patients with respiratory conditions, with a VI of 0.039. Following PM10, the pollutants with the next highest coefficients of importance were NO2 (VI = 0.034), SO2 (VI = 0.033), PM2.5 (VI = 0.029), and CO (VI = 0.025), respectively. Furthermore, the study observed a notable increase in the mortality rate of respiratory patients over time. Specifically, for every five days, the death rate increased by 35.5%. Indeed, climate change and air pollution significantly contribute to the mortality rate from respiratory diseases. Therefore, it is crucial for individuals, particularly those with respiratory conditions, to heed meteorological warnings.

Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California

California’s diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989–2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively.Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations.To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)–NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions.Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

Recent Trends in Air Pollution in the Most Important City of The Romanian Black Sea Coast

Air pollution is one of the greatest environmental issues of contemporary society, affecting virtually all major cities worldwide, including those in Romania. Consequently, evaluating the dynamics of pollution conditions in urban environments is essential for designing control measures and adaptation strategies to this environmental disruption. This paper aims to analyze the recent dynamics of pollution and air quality in Constanta, the largest city on the Romanian coastline and one of the most important cities along the entire Black Sea coast. The investigations were based on the official concentrations of major atmospheric pollutants recorded at air quality monitoring stations located within or near Constanta. The dynamics of pollutant concentrations were explored at annual and seasonal levels, and the results showed a mixed picture of changes in the atmospheric pollutant concentrations, in recent years. Essentially, the findings highlighted both increases and decreases in air pollution conditions, emphasizing the need for constant air quality monitoring and controlling throughout Constanta city.

TKSTAGNet: A Top-K Spatio-Temporal Attention Gating Network for air pollution prediction

Accurate air pollution prediction is of great significance for human health, travel, and environmental management. As an emerging artificial intelligence technology, Transformer has already shown tremendous potential in air pollution prediction. Unfortunately, existing Transformer-based methods are challenging to accurately predict air pollution due to two limitations: (1) they neglect irrelevant spatial and temporal neighbors (e.g., neighboring sensors and timesteps) when modeling spatio-temporal correlations, and (2) they lack an adaptive mechanism to mine different effects caused by various external factors (e.g., meteorological conditions). Jointly taking these limitations into account, we propose a novel Transformer-based architecture for air pollution prediction, named Top-K Spatio-Temporal Attention Gating Network (TKSTAGNet). In this architecture, to effectively extract spatio-temporal correlations, we first design a TopKpooling Spatial Attention (TopK-SA) and a TopKpooling Temporal Attention (TopK-TA). In TopK-SA, the TopKpooling mechanism is introduced into spatial attention to filter out irrelevant spatial neighbors, accurately capturing spatial dependencies. In TopK-TA, the TopKpooling mechanism is integrated with temporal attention to identify and exclude irrelevant temporal neighbors, precisely extracting temporal dependencies. To adaptively fuse various external factors, a gating fusion module is then proposed. We conduct experiments on three real-world air pollution datasets from urban regions in Beijing, Shanghai, and Chongqing, respectively. Experimental results demonstrate that TKSTAGNet outperforms state-of-the-art baselines in prediction precision and fitting the variation trends of air pollution.

Evaluation of trends in air pollution and daily mortality in The Netherlands over the period 1995 - 2019

Evaluation of trends in air pollution and daily mortality in The Netherlands over the period 1995 - 2019

Estimating global trends of air pollution, air pollution-attributable disease burdens, and CO2 emissions in 13,000 cities using large geospatial datasets

Estimating global trends of air pollution, air pollution-attributable disease burdens, and CO2 emissions in 13,000 cities using large geospatial datasets

Study on the establishment of air pollutant and carbon emission inventory and collaborative emission reduction potential of China's coking industry from 2012 to 2022

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, NOx, VOCs, and CO2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, NOx, VOCs and CO2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Air pollution and corporate financial assets allocation: Evidence from China

Understanding the relationship between air pollution and corporate investment strategy has significant implications for both environmental sustainability and economic development. Utilizing data from China's A-share listed companies spanning from 2014 through 2022, we investigate the causal relationship between air pollution and corporate allocation of financial assets. Using an instrumental variable approach, we find that worse air quality has a positive effect on corporate financial asset allocation. Specifically, a 1% increase in the number of air pollution days tends to result in a 0.002% increase in the share of financial assets held by firms. This effect prevails through diverse robustness tests. Furthermore, as companies encounter more stringent financing constraints and environmental regulations, the trend of air pollution promoting the increased financialization of assets becomes more pronounced. Based on the findings, this paper suggests providing more support for enterprises to facilitate the upgrading of production and operation to meet environmental standards.

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.

Environmental Insights: Democratizing access to ambient air pollution data and predictive analytics with an open-source Python package

Ambient air pollution is a pervasive issue with wide-ranging effects on human health, ecosystem vitality, and economic structures. Utilizing data on ambient air pollution concentrations, researchers can perform comprehensive analyses to uncover the multifaceted impacts of air pollution across society. To this end, we introduce Environmental Insights, an open-source Python package designed to democratize access to air pollution concentration data. This tool enables users to easily retrieve historical air pollution data and employ a Machine Learning model for forecasting potential future conditions. Moreover, Environmental Insights includes a suite of tools aimed at facilitating the dissemination of analytical findings and enhancing user engagement through dynamic visualizations. This comprehensive approach ensures that the package caters to the diverse needs of individuals looking to explore and understand air pollution trends and their implications. Code repository clickable linkEnvironmental Insights Github Home Page.

Unveiling pollutants in Sonipat district, Haryana: Exploring seasonal, spatial and meteorological patterns

Air pollution is a serious environmental problem that aggravates climate change. Sonipat district, is a fast-urbanizing area with serious air pollution issues brought on by a variety of industry, busy traffic, and agricultural methods. Since in-situ measurements of air pollution are not feasible everywhere on Earth's surface, satellite data sets are crucial for determining trends in air pollution. This study uses several data sets, such as Sentinel-5P air pollution data, CHIRPS rainfall data, and GPWv4 population density data to investigate the spatial and temporal fluctuations of air pollution with reference to meteorological and other important socioeconomic factors. Pollutants include Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), and Absorbing Aerosol Index (AAI) are the main focus of the analysis during the winter, summer, and post-monsoon seasons. Findings revealed that the district's southeast portion has greater concentrations of CO and NO2, which are likely due to the intense traffic and industrial activity along National Highway 44. While O3 and SO2 exhibit seasonal fluctuations correlated with temperature and sunlight, AAI levels peak in the October in post-monsoon season as a result of regional wind patterns and agricultural burning. Rainfall during the September in post-monsoon season lowers the concentrations of pollutants because of retreating monsoon. Pollutant dispersion is greatly impacted by wind direction and speed. The study highlights the significance of weather-related elements in influencing air pollution levels and emphasizes the necessity for efficient air quality management and pollution mitigation techniques.

Seasonal and annual emission trends of PM2.5 and PM10 over the national capital Delhi from 2015 to 2020

Introduction: Many urban cities in India, including National Capital Territory (NCT) of Delhi, have been adversely affected by air pollution since 2010s. Factors like atmospheric circulation, topography, local weather patterns, vehicular emission, industrial activities, waste burning etc. lead to extreme air pollution which is a growing threat to public health. Materials and methods: The study was conducted to examine severity of air quality in Delhi from 2015 to 2020. For this purpose, Particulate Matters (PM2.5 and PM10) measurements were done at ITO, Mandir Marg, Punjabi Bagh, Anand Vihar, and R K Puram for a period of 2015 to 2020. Everyday data was collected to interpret and analyze the trend of air pollution both seasonally and annually. Results: There was a significant increase in particulate matter levels at ITO, Mandir Marg, and RK Puram in the year 2016 while in the case of Anand Vihar the highest levels were measured in 2017. Also, Punjabi Bagh showed the highest PM10 levels in 2017. The annual average data for all five sites indicates that PM2.5 and PM10 levels were above the air quality standard throughout the study period. Anand Vihar showed the highest level of particulate matter among all five locations during the study period of 2015 to 2020. The change in seasons is also one of the important factors affecting air quality. Overall, PM2.5 and PM10 concentration were recorded maximum during winters i.e. 218.07 μg/m3 and 358.80 μg/m3 respectively. Conclusion: The results conclude that air quality deteriorated more during winters>autumn>summers>monsoon. Also, the air quality index (AQI) recorded for Delhi during the study period was highest in 2016, respectively. Though AQI decreased over the years, it lied in poor quality only

Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine

California has a long history of wildfires that release substantial amounts of smoke, contributing to regional air pollution. This study employs advancements in Earth observation satellites, cloud computing, and the Google Earth Engine to analyze MODIS MCD19A2 Version 6 level 2, Sentinel-5P NRTI AER AI, and Sentinel-5P NRTI NO2, evaluating the impact of wildfires from 2010 to 2022 on air quality in California. Historical fire events are cross-validated using the MODIS 1-km MYD14A1 V6 dataset, and the MODIS MAIAC-derived Aerosol Optical Depth (AOD) values are validated against AERONET AOD measurements from ground-based sun photometers. To assess the analysis's uncertainty, metrics such as Mean Absolute Error, Relative Mean Bias, and Root Mean Square Error are applied. Additionally, linear regression is used to verify the correlation between satellite and ground measurements. Results show that the average monthly MODIS MAIAC AOD at 470 nm and 550 nm tends to overestimate AOD by 24% and 6%, respectively, compared to AERONET AOD values. The correlation coefficient and adjusted R-squared value range from 0.78 to 0.80 and from 0.61 to 0.65, respectively, for AOD values at 470 nm and 550 nm. During the fire season, correlations exhibit closer associations, with a coefficient above 0.78 and an adjusted R-squared value above 0.63 are observed for both wavelengths. Sentinel-5P data reveal a significant increase in NO2 concentration in the surrounding areas during the 2020 and 2021 wildfires. This study identifies historical spatiotemporal trends of air pollution attributable to wildfires in California from 2010 to 2022, informing decision-making in the development of effective prevention and mitigation programs.

Long-term pollen season trends of Fraxinus (ash), Quercus (oak) and Ambrosia artemisiifolia (ragweed) as indicators of anthropogenic climate change impact

The ongoing climatic change, together with atmospheric pollution, influences the timing, duration and intensity of pollen seasons of some allergenic plant taxa. To study these influences, we correlated the trends in the pollen season characteristics of both woody (Fraxinus, Quercus) and herbaceous (Ambrosia) taxa from two pollen monitoring stations in Slovakia with the trends in meteorological factors and air pollutants during the last two decades. In woody species, the increased temperature during the formation of flower buds in summer and autumn led to an earlier onset and intensification of next year’s pollen season, especially in Quercus. The increase of relative air humidity and precipitation during this time also had a positive influence on the intensity of the pollen season of trees. The pollen season of the invasive herbaceous species Ambrosia artemisiifolia was prolonged by increased temperature and humidity during the summer and autumn of the same year, which extended the blooming period and delayed the end of the pollen season. From the studied air pollutants, only three were found to correlate with the intensity of the pollen season of the studied taxa, CO − positively and SO2 and NO2 − negatively. It is important to study these long-term trends since they not only give us valuable insight into the response of plants to changing conditions but also enable the prognosis of the exacerbations of pollen-related allergenic diseases.

The Long Hazy Tail: Analysis of the Impacts and Trends of Severe Outdoor and Indoor Air Pollution in North China.

China, especially the densely populated North China region, experienced severe haze events in the past decade that concerned the public. Although the most extreme cases have been largely eliminated through recent mitigation measures, severe outdoor air pollution persists and its environmental impact needs to be understood. Severe indoor pollution draws less public attention due to the short visible distance indoors, but its public health impacts cannot be ignored. Herein, we assess the trends and impacts of severe outdoor and indoor air pollution in North China from 2014 to 2021. Our results demonstrate the uneven contribution of severe hazy days to ambient and exposure concentrations of particulate matter with an aerodynamic diameter <2.5 (PM2.5). Although severe indoor pollution contributes to indoor PM2.5 concentrations (23%) to a similar extent as severe haze contributes to ambient PM2.5 concentrations (21%), the former's contribution to premature deaths was significantly higher. Furthermore, residential emissions contributed more in the higher PM2.5 concentration range both indoors and outdoors. Notably, severe haze had greater health impacts on urban residents, while severe indoor pollution was more impactful in rural areas. Our findings suggest that, besides reducing severe haze, mitigating severe indoor pollution is an important aspect of combating air pollution, especially toward improving public health.

Decadal Trends in Ambient Air Pollutants and Their Association with COPD and Lung Cancer in Upper Northern Thailand: 2013-2022.

Air pollution in upper northern Thailand raises health concerns. This study examined trends and associations between air pollutants and respiratory diseases, focusing on COPD and lung cancer during haze (December-May) and non-haze (June-November) seasons in upper northern Thailand from 2013 to 2022. This study utilized data from the Pollution Control Department and Chiang Mai Provincial Public Health. The key air pollutants included PM10, PM2.5, SO2, NO2, CO, and O3. Respiratory disease data included fatality rates for lung cancer and COPD and the re-admission rate for COPD. Results indicated peak air pollutant levels and COPD re-admission rates in March, with PM2.5 concentrations exceeding air quality standards from January to April. During haze periods, COPD fatality and re-admission rates significantly increased (mean difference: 0.43 and 4.23 per 1000-case population, respectively; p < 0.001), while lung cancer fatality rates were higher without statistical significance. Pearson correlation analysis found positive correlations between PM10, PM2.5, O3, and NO2 concentrations and COPD re-admission and fatality rates at 0-1 month lag times, with a declining trend observed at subsequent lag intervals of 2 to 3 months. Overall, this study highlights the predictable pattern of air pollution in the region, correlating with higher COPD fatality and re-admission rates.

The effect of growth, deforestation, forest fires, and volcanoes on Indonesian regional air quality

As a rapidly developing country, Indonesia faces a challenge in improving air quality that is made more difficult by frequent forest fires linked to deforestation and volcanic eruptions. This paper analyses the link between development and air pollution in 30 Indonesian cities from 2002 to 2019. Air quality is measured using two novel regional air quality indicators (AQIs) for PM2.5 and PM10 estimated from weather observations and NASA satellite data on Aerosol Optical Depth (AOD). Results from an Environmental Kuznets Curve (EKC) analysis, based on a dynamic panel data model, show that forest fires and volcanic activity have significantly worsened regional air quality in Indonesia. The model also shows that the EKC hypothesis is rejected for Indonesia as cities have followed a monotonically increasing trend in air pollution with respect to economic growth. An additional 1% of economic growth increased long run pollution as measured by AQI PM2.5 and PM10 by 0.35% and 0.46%, and forest fire events by 0.11% and 0.06%. A partial Benefit-Cost Analysis (BCA) of forest fires shows that particulate matter pollution is excessive. Decreasing the frequency of forest fires by 1% would generate a net benefit of between US$17 million to 145 million. The lower estimate of the health benefits exceeds the agricultural benefits of forest fire without accounting for other non-market costs due to forest fires. Our results imply that, as Indonesia grows economically, air quality is likely to continue deteriorating. Further, regions affected by regular volcanic activity and wildfires should apply tighter emission standards to controllable point source emissions from industry and non-point source emissions such as agricultural forest fires.

The evaluation of forty years of air quality and trend of air pollutants in Erzurum City

The evaluation of forty years of air quality and trend of air pollutants in Erzurum City