Air Quality Trends Research Articles

A Comprehensive Analysis of Santiago's Air Quality Forecasts Using SARIMA Model

Abstract. This study analyses Santiagos air quality by forecasting key pollutant concentrations- PM2.5, PM10, NO, and O-over June 2024 to May 2025 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Historical data from 2016 to 2024 shows seasonal peaks, particularly in winter, driven by residential heating and atmospheric conditions. A notable decrease in annual peak NO level, from 71 g/m in 2019 to 23 g/m in 2020, likely due to the COVID-19 pandemic and air quality initiatives. The SARIMA forecasts indicate that PM2.5 levels could reach up to 80 g/m, NO around 28 g/m, O around 47ppb and PM10 around 45g/m during peak periods, highlighting ongoing seasonal pollution challenges. To address these, policymakers should enforce stricter emissions regulations, promote cleaner heating, expand public transportation, and improve air quality monitoring. Additional strategies like traffic management, urban greening, and preparedness for high-pollution days are also recommended. This study demonstrates the SARIMA models effectiveness in forecasting air quality trends, offering valuable insights for policy development to protect public health in Santiago.

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77µg/m3, CO: 1.41 ± 0.84mg/m3; traffic site: PM10: 58.67 ± 20.05µg/m3, CO: 0.99 ± 0.57mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

Major air pollution and climate policies in NYC and trends in NYC air quality 1998-2021.

Air pollution poses serious health risks to humans, with particular harm to children. To address the gap in understanding the efficacy of policies to reduce exposure to air pollution, we sought to assess the temporal relationship between the enactment of major air pollution and climate policies in NYC and trends in air quality during the period 1998-2021. We used previously available data from citywide monitoring and new data from the Columbia Center for Children's Environmental Health (CCCEH) longitudinal cohort studies of mothers and children living in communities in Northern Manhattan and the South Bronx. We utilized publicly available citywide air monitoring data for particulate matter (PM2.5) and nitrogen dioxide (NO2) from 2009 to 2021 from the New York City Community Air Survey (NYCCAS) database and CCCEH cohort data on residential exposure to PM2.5 and NO2 and personal exposure to polycyclic aromatic hydrocarbons (PAH) during pregnancies occurring from 1998-2016 and 1998-2021, respectively. We compared annual and overall reductions in PM2.5 and NO2 citywide and reductions in PAH concentrations in the cohort studies. As previously reported, annual average concentrations of pollutants in NYC dropped significantly over time. Between 1998 and 2021, PM2.5 and NO2 concentrations were reduced citywide by 37 and 31%, respectively. In our CCCEH cohorts, between 1998 and 2016, the annual average PM2.5 and NO2 concentrations also decreased significantly by 51 and 48%, respectively. Between 1998 and 2020, PAH concentrations decreased significantly by 66%. While it is not possible to link improved air quality to a single policy, our analysis provides evidence of a cumulative beneficial effect of clean air and climate policies enacted between 1998 and 2021 both city-wide and in our cohorts residing in communities that have been disproportionately affected by air pollution. There are important implications for health benefits, particularly for children, who are known to be especially vulnerable to these exposures. The results support further environmental and social policy changes to prevent the serious health impacts of air pollution from fossil fuel emissions.

Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database

Machine-learning air pollution prediction studies are widespread worldwide. This study examines the use of machine learning to predict air pollution, its current state, and its expected growth in India. Scopus was used to search 326 documents by 984 academics published in 231 journals between 2007 and 2023. Biblioshiny and Vosviewer were used to discover and visualise prominent authors, journals, research papers, and trends on these issues. In 2018, interest in this topic began to grow at a rate of 32.1 percent every year. Atmospheric Environment (263 citations), Procedia Computer Science (251), Atmospheric Pollution Research (233) and Air Quality, Atmosphere, and Health (93 citations) are the top four sources, according to the Total Citation Index. These journals are among those leading studies on using machine learning to forecast air pollution. Jadavpur University (12 articles) and IIT Delhi (10 articles) are the most esteemed institutions. Singh Kp's 2013 "Atmospheric Environment" article tops the list with 134 citations. The Ministry of Electronics and Information Technology and the Department of Science and Technology are top Indian funding agency receive five units apiece, demonstrating their commitment to technology. The authors' keyword co-occurrence network mappings suggest that machine learning (127 occurrences), air pollution (78 occurrences), and air quality index (41) are the most frequent keywords. This study predicts air pollution using machine learning. These terms largely mirror our Scopus database searches for "machine learning," "air pollution," and "air quality," showing that these are among the most often discussed issues in machine learning research on air pollution prediction. This study helps academics, professionals, and global policymakers understand "air pollution prediction using machine learning" research and recommend key areas for further research.

Pembangunan Sistem Informasi Pelaporan Masalah Lingkungan Berbasis Web di Universitas Kristen Satya Wacana

Meeting environmental challenges necessitates the exploration of new and inventive solutions. The Environmental Quality Index (IKLH) report revealed an uptick in Indonesia's index to 72.42 in 2022 from 71.45 in 2021, despite a worsening air quality trend. Acknowledging the importance of Human Resource Management and "Go Green" education in promoting environmental consciousness, this paper proposes the adoption of Laravel for expeditious handling of environmental issues at UKSW. Through the utilization of the Waterfall model and UML methodology, the system integrates MVC architecture, streamlining the process of environmental problem reporting for students. Rigorous Black Box Testing ensures the seamless functionality of the system. Ultimately, this initiative seeks to fortify the university's environmental stewardship while advancing the Go Green campaign, thereby fostering a campus environment that is both pristine and sustainable.

Long-Term Monitored Norway Spruce Plots in the Ore Mountains-30 Years of Changes in Forest Health, Soil Chemistry and Tree Nutrition after Air Pollution Calamity.

The Ore Mountains were historically one of the most polluted areas in Europe, where high sulphur dioxide concentrations and a high level of atmospheric deposition led to a vast decline in Norway spruce stands in the mountain ridge plateau. In this article, we evaluate the trends in the atmospheric deposition load, soil chemistry, tree nutrition, crown defoliation and height increment in a network of twenty research plots monitored for last thirty years in this region. The decrease in sulphur and nitrogen deposition was most pronounced at the end of 1990s. Extreme values of sulphur deposition (100-200 kg.ha-1.year-1) were recorded in throughfall under mature Norway spruce stands in the late 1970s, and after felling of the damaged stand, the deposition levels were comparable to open plot bulk deposition. Nitrogen deposition decreased more slowly compared with sulphur, and a decrease in base cation deposition was observed concurrently. The current deposition load is low and fully comparable to other mountain areas in central Europe. Accordingly, the health of young spruce stands, as assessed by defoliation and height increment, has improved and now corresponds to the Czech national average. On the other hand, no significant changes were observed in the soil chemistry, even though some of the plots were limed. Acidic or strongly acidic soil prevails, often with a deficiency of exchangeable calcium and magnesium in the mineral topsoil, as well as decreases in available phosphorus. This is reflected in the foliage chemistry, where we see an imbalance between a relatively high content of nitrogen and decreasing contents of phosphorus, potassium and calcium. Despite the observed positive trends in air quality and forest health, the nutritional imbalance on acidified soils poses a risk for the future of forest stands in the region.

Mapping air quality trends across 336 cities in India: Insights from three decades of monitoring (1987–2019)

Over a span of 34 years (1987–2019), an in-depth analysis of PM10, SO2, and NO2 trends across India was conducted using data from the National Ambient Air Quality Monitoring Programme’s manual monitoring stations in 336 cities. The study encompassed six geographical regions over three time blocks, revealing a correlation between the expansion of monitoring networks and the nation’s economic growth. Regions like the densely populated Indo-Gangetic Plains (IGP) and Central India consistently hosted more monitoring stations, while the Himalayan and Northeast regions saw substantial increases from initial scarcity. SO2 concentrations showed a declining trend, while NO2 levels remained relatively stable with intermittent fluctuations. Conversely, national average PM10 concentrations exhibited an upward trajectory, notably spiking by 128 % between 2006 and 2009 due to economic activities, construction, network expansion, the 2009 drought, and heightened coal consumption. Spatially, pollutant concentrations across three blocks demonstrated improved SO2 levels, several cities exceeding NO2 standards, and persistently high PM10 levels in the IGP. PM10 levels in block 3 were lower than in block 2, reflecting effective policy interventions. State rankings, however, did not consistently reflect pollutant trends across blocks. Regionally, the IGP consistently had the highest PM10 concentrations, while the Northeast recorded the lowest. Population-weighted exposure levels indicated an overall increase in public exposure to PM10. Analysis of major city per region aligned with national trends, as evidenced by Delhi (IGP), Guwahati (Northeast), Vadodara (Northwest), and Bhopal (Central) showing increased PM10 concentrations since 2006, followed by intermittent declines. In contrast, Shimla (Himalayan) and Chennai (Southern) exhibited distinct patterns. Major industrial cities such as Parwanoo, Bongaigaon, Angul and Talcher, and Visakhapatnam mirrored national trends, with PM10 levels rising since 2009, highlighting the significant impact of industrial activities on air quality. This research underscores the need for targeted, effective mitigation strategies based on spatial and temporal pollutant trends.

Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration

Low-cost sensor networks offer the potential to reduce monitoring costs while providing high-resolution spatiotemporal data on pollutant levels. However, these sensors come with limitations, and many aspects of their field performance remain underexplored. During October to December 2023, this study deployed two identical low-cost sensor systems near an urban standard monitoring station to record PM2.5 and PM10 concentrations, along with environmental temperature and humidity. Our evaluation of the monitoring performance of these sensors revealed a broad data distribution with a systematic overestimation; this overestimation was more pronounced in PM10 readings. The sensors showed good consistency (R2 > 0.9, NRMSE<5 %), and normalization residuals were tracked to assess stability, which, despite occasional environmental influences, remained generally stable. A lateral comparison of four calibration models (MLR, SVR, RF, XGBoost) demonstrated superior performance of RF and XGBoost over others, particularly with RF showing enhanced effectiveness on the test set. SHAP analysis identified sensor readings as the most critical variable, underscoring their pivotal role in predictive modeling. Relative humidity consistently proved more significant than dew point and temperature, with higher RH levels typically having a positive impact on model outputs. The study indicates that, with appropriate calibration, sensors can supplement the sparse networks of regulatory-grade instruments, enabling dense neighborhood-scale monitoring and a better understanding of temporal air quality trends.

Species habitat suitability increased during COVID-19 lockdowns

COVID-19 lockdowns had strong positive and negative effects on the environment and biodiversity. The most evident effects were the occurrence of wildlife in the middle of urban settlements, the reduction of noise, and the improvement in air quality. However, other effects on species are less well known. Our main question is: did species habitat suitability increase during lockdowns? Here, we analysed trends in species habitat suitability over time by modelling the species distributions with a time series of environmental variables obtained from satellite remote sensing. We modelled the habitat suitability of 381 species of vascular plants, amphibians, reptiles, birds, and mammals, before, during, and after the lockdowns in the Iberian Peninsula (Portugal and Spain), from July 2017 to August 2022 with the Maxent presence-background algorithm and five MODIS variables (Albedo, Evapotranspiration, LST, NDVI, and Surface reflectance) with a periodicity of 8 days, and analysed their trends with the Mann-Kendall test. We compared our results with air quality data (PM10, CO, and NO2) from ground stations and Sentinel 5 P and with mobility data from Google mobility reports. Habitat suitability increased during both lockdowns when considering all species together. The peak in species' habitat suitability trends during the first lockdown was coincident with air quality and mobility trends: air pollution decreased when people stayed at home. Reduction in human mobility during COVID-19 lockdowns revealed positive effects on species’ habitat quality, highlighting the importance of reducing human activities as the only solution for nature conservation.

Predicting air quality trends in Malaysia’s largest cities: the role of urban population dynamics and COVID-19 effects

This paper aims to explore the relationship between the Air Quality Index (AQI), COVID-19 incidence rates, and population density within Malaysia’s ten most populous cities from January 2018 to December 2021. Data were sourced from the Department of Statistics Malaysia, the World Air Quality Index Project, and Our World in Statistics. The methodology integrated population-based city classification and AQI assessment, cluster analysis through SPSS, and Generalized Additive Mixed Model (GAMM) analysis using R Studio despite encountering a data gap in AQI for five months in 2019. Cities were organized into three clusters based on their AQI: Cluster One included Ipoh, Penang, Kuala Lumpur, and Melaka, Cluster Two comprised Kuantan, Seremban, Johor Bahru, and Kota Bharu, Cluster Three featured Kota Kinabalu and Kuching. GAMM analysis revealed prediction accuracies for AQI variations of 58%, 60%, and 41% for the respective clusters, indicating a notable impact of population density on air quality. AQI variations remained unaffected by COVID-19, with a forecasted improvement in air quality across all clusters. The paper presents novel insights into the negligible impact of COVID-19 on AQI variations and underscores the predictive power of population dynamics on urban air quality, offering valuable perspectives for environmental and urban planning.

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

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

Short-term trends of air quality and pollutant concentrations in Nigeria from 2018–2022 using tropospheric sentinel-5P and 3A/B satellite data

This study presents a comprehensive analysis of atmospheric parameters in Nigeria, utilizing Sentinel-5P and 3A/B offline datasets from April 2018 to December 2022. The research evaluates the concentrations of pollutants such as Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), Formaldehyde (HCHO), Methane (CH4), Carbon Monoxide (CO), and Aerosol Indices (AI) across different periods. The data analysis employs multivariate techniques, including Factor Analysis, Pearson Correlation Coefficient, Scatter Plots, and Dendrogram Clustering, providing insights into the interrelationships and potential sources of pollution. Additionally, the study examines monthly trends, temperature variations, and their impacts on human health and climate. The findings reveal stable levels of NO2, SO2, O3, and HCHO with minimal fluctuations, while CH4 and CO exhibit slight variations, possibly influenced by emission reductions or atmospheric changes. Aerosol optical indices consistently indicate aerosol presence, influenced by local emissions and atmospheric processes. Multivariate analyses highlight shared patterns among atmospheric constituents, shedding light on potential common sources and chemical relationships. The Pearson correlation coefficients illustrate the strength and direction of linear relationships, offering valuable information on pollution dynamics. The study's visualizations, including scatter plots and dendrogram clustering, enhance the understanding of complex interactions. Overall, the research underscores the significance of continuous monitoring and research efforts for informed decision-making and environmental management in Nigeria.

Air Quality Assessment and Trends in Non-Attainment Cities of Assam (India)

With rapid industrialisation and development in the country, air pollution has posed a major threat to the health of every individual. In the current study, the levels of air pollutants (such as PM, SOx, NOx) of the 5 non-attainment cities of Assam i.e. Nalbari, Nagaon, Guwahati, Sivasagar and Silchar, were compared from the year 2017 to 2021. The study showed that the Air Quality Index (AQI) of Nalbari, Sivasagar and Silchar varied from good to moderate but for Guwahati and Nagaon, it varied from moderate to satisfactory. PM10 was found to be the major contributor. Furthermore, the exceedance factor (EF) for SOx and NOx for all five cities was determined to be L (Low pollution) i.e. less than 0.5, except for Guwahati's SOx concentration, which was M (Moderate pollution) i.e. between 0.5 and 1.0 in 2021. But EF of PM10 varied from M to C (Critical pollution) i.e. more than 1.5 for all the 5 cities from 2017 to 2021. Although, specific initiatives and sustainable techniques were initiated in 2019 under the Nation Clean Air Program (NCAP) to tackle the serious air quality issues in the selected non-attainment cities of Assam; it was found that the PM10 concentrations of Guwahati and Nalbari were found to be high in 2021 i.e. 114 μg/m3 and 92 μg/m3 as compared to 2019 which was 96.7 μg/m3 and 87 μg/m3 respectively. Hence, strict implementation of effective action plans under NCAP along with additional steps that include changes in the lifestyle, will play a major role in improving the air quality of Assam.

Quantitative analysis of air pollution levels and its health implications in Delhi-NCR: A longitudinal study

Air pollution is a pressing global issue, particularly acute in rapidly urbanizing regions like Delhi-NCR. This research presents a longitudinal study to quantitatively analyze air pollution levels and their health implications in Delhi-NCR. The research objectives include assessing air quality trends, examining the relationships between various pollutants, and investigating the impact of air pollution on respiratory admissions. Data was sourced from the Ambient Air Quality Monitoring Network, covering the years 2020 to 2022, focusing on key pollutants, including PM2.5, PM10, NO2, SO2, CO, and O3. A range of data analysis techniques, including correlation analysis, regression analysis, longitudinal data analysis, spatial analysis, and compliance with WHO guidelines, were applied. Key findings highlight persistently high levels of air pollutants, strong interrelations among pollutants, and consistent respiratory admissions, especially in winter. Regression analysis confirms a significant link between PM2.5 and respiratory admissions. Longitudinal analysis demonstrates the long-term health consequences of air pollution, and spatial analysis reveals district-wise variations in air quality. The study also emphasizes seasonal strategies, compliance with international guidelines, and socioeconomic disparities in health outcomes. This research has broader implications for evidence-based policymaking, emphasizing the urgency of addressing air quality concerns. It calls for targeted interventions, district-level strategies, season-specific measures, and equitable access to healthcare. These findings offer a comprehensive basis for addressing the air quality crisis in Delhi-NCR, ultimately promoting the well-being of its residents.

AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition

The integration of multi-source sensors based AIoT (Artificial Intelligence of Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields of sustainable and smart environmental design, urban development, and pollution control. This study focuses on enhancing the prediction of emission, with a special emphasis on pollutants, utilizing advanced deep learning (DL) techniques. Recurrent neural networks (RNNs) and long short-term memory (LSTM) neural networks have shown promise in predicting air quality trends in time series data. However, challenges persist due to the unpredictability of air quality data and the scarcity of long-term historical data for training. To address these challenges, this study introduces the AIoT-enhanced EEMD-CEEMDAN-GCN model. This innovative approach involves decomposing the input signal using EEMD (Ensemble Empirical Mode Decomposition) and CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to extract intrinsic mode functions. These functions are then processed through a GCN (Graph Convolutional Network) model, enabling precise prediction of air quality trends. The model’s effectiveness is validated using air pollution datasets from four provinces in China, demonstrating its superiority over various deep learning models (GCN, EMD-GCN) and series decomposition models (EEMD-GCN, CEEMDAN-GCN). It achieves higher accuracy and better data fitting, outperforming other models in key metrics such as MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and R2 (Coefficient of Determination). The implementation of this AIoT-enhanced model in air pollution prediction allows decision-makers to more accurately anticipate changes in air quality, particularly concerning carbon emissions. This facilitates more effective planning of mitigation measures, improvement of public health, and optimization of resource allocation. Moreover, the model adeptly addresses the complexities of air quality data, contributing significantly to enhanced monitoring and management strategies in the context of sustainable urban development and environmental conservation.

Exploring Global Air Pollution Dynamics: Data Analysis and Insights for Environmental Health and Policy

Air pollution is a significant global environmental concern with detrimental impacts on human health and ecosystems. In this research project, we analyze global air pollution data sourced from various regions worldwide. The dataset includes information on air quality index (AQI) categories, particulate matter (PM2.5) levels, and other relevant parameters. Our study employs Pandas, Matplotlib, and Seaborn libraries in Python for data processing, visualization, and analysis. We begin by filtering out data points associated with "Unhealthy for Sensitive Groups" AQI category to focus on broader air quality trends. Subsequently, we explore the basic characteristics of the dataset, including summary statistics and missing value detection. Visualization techniques such as count plots and correlation heat maps are utilized to illustrate the distribution of AQI categories and assess relationships between different variables, respectively. Our findings contribute to a better understanding of global air pollution patterns, which can inform policy-making and environmental management strategies aimed at mitigating the adverse effects of air pollution on public health and the environment. Index Terms— Particulate matter (PM2.5), Air quality index (AQI), Public health, Environmental management, Air pollution, Global

Can aerosol optical depth unlock the future of air quality monitoring and lung cancer prevention?

AbstractThis “Policy Brief” explores the potential integration of Aerosol Optical Depth (AOD) into the United Kingdom’s air quality and public health monitoring frameworks, highlighting its potential to enhance existing air pollution control strategies. Amid growing concerns over air pollution’s impact on health, particularly the link between particulate matter and lung cancer, this brief presents a focused investigation into how AOD can be leveraged alongside traditional monitoring methods to provide a more nuanced understanding of air quality trends. By correlating AOD data with lung cancer incidence rates within the UK, the brief aims to uncover potential associations and inform public health decisions. Furthermore, it discusses the advantages and limitations of employing AOD in air pollution and respiratory disease monitoring, advocating for a strategic enhancement of the UK's air pollution monitoring efforts. This approach seeks to complement and refine current monitoring practices with advanced remote sensing techniques, aiming to inform policy innovations that prioritize environmental health and public welfare. Through a comprehensive review of existing data and policies, the brief underscores the urgency of adopting multidimensional air quality management strategies that respond to technological advancements and emerging public health needs.

Long-term trends in British Columbia lower mainland air quality: Criteria air pollutants and VOC

ABSTRACT The lower mainland of British Columbia is a geographic region that comprises the districts of Metro Vancouver and the Lower Fraser Valley. It is situated in a complex topographical and coastal location in southwestern British Columbia. Metro Vancouver is Canada’s third largest population center. Accessing the Canadian National Air Pollution Surveillance Program (NAPS) database we calculated air pollutant statistics using the Canadian Ambient Air Quality Standards (CAAQS) averaging times, numerical forms, and numerical levels for the years 2001to 2020. Man Kendall and Sen statistical methods were used to test for the presence of trends and the slope of those trends in fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and volatile organic compound (VOC) ambient air concentrations. We did not determine a significant trend in 98th percentile of the daily 24-hr average PM2.5 concentrations. We did determine significant negative trends in the annual average of the daily 24-hr average PM2.5 concentrations at 6 of the 9 locations. Episodic, multi-day duration elevated PM2.5 concentrations related to forest fires were a significant influence on PM2.5 ambient concentrations. Annual 4th highest daily maximum 8-hr average O3 concentrations showed no trend at 14 of 18 locations, declined at 3 locations, and increased at one location. We determined statistically significant declines in peak and average NO2 and SO2 concentrations, and in time-integrated annual VOC concentrations.

Predictions of air quality and challenges for eliminating air pollution during the 2022 Olympic Winter Games

Scientifically and efficiently ensuring good air quality for important events is an issue of concern to the government. In addition to analysis based on historical data, advanced prediction before an event is essential for the government having ample time to take effective actions to improve air quality during the event period. Taking “the 2022 Olympic Winter Games (OWG)” as a typical case, a chemical transport model coupled with a tracer-tagged module was used to evaluate the air quality and source apportionment of ambient pollutants in the OWG host cities under historical and predicted meteorological conditions. Driven by the downscaling of meteorological fields from an operational real-time climate forecast system, the potential ability of air quality forecasting three months ahead was investigated, which was meaningful for designing control strategies. Sensitive simulations indicated that under unfavorable meteorological conditions, such as those during February 2014, both Beijing and Zhangjiakou faced a high risk of experiencing haze episodes, even based on current anthropogenic emission intensity. The contribution of the joint prevention and control region to Beijing and Zhangjiakou would become larger under worse meteorological conditions, which favor heavy air pollution. The source apportionment results indicated that strengthened emission control in cities including Beijing, Zhangjiakou and south of Beijing (Baoding, Langfang, Tianjin, and Tangshan) is effective for reducing haze episodes in the host cities. There is still a long way to make accurate daily fine particulate matter predictions on a seasonal-scale in advance; however, it could capture the trends in air quality in host cities around the OWG period three months ahead. The comparison of observations and predictions confirmed and highlighted the role of regional emission controls on the realization of the “OWG blue”.

FORECASTING AIR POLLUTION LEVELS IN TOURIST AREAS IN KAZAKHSTAN USING ARTIFICIAL INTELLIGENCE METHOD

This article discusses the use of convolutional neural networks (CNN) to assess air pollution levels in tourist areas and its effects on the tourism industry. Air pollution poses serious challenges to public health and environmental sustainability, especially in regions frequented by tourists. CNN algorithms offer a powerful tool for analyzing air quality based on images collected from a variety of sources, in-cluding satellite data, unmanned aerial vehicles and ground-based sensors. By processing and analyzing these images, CNNS can detect pollution hotspots, track pollution sources, and predict air quality trends. The introduction of CNN-based air quality analysis in tourist destinations provides a number of benefits, including the creation of early warning systems, improved planning and management, promotion of sustainable tourism practices and reputation management. However, in order to realize the full poten-tial of CNN algorithms in this context, it is necessary to solve problems such as data availability, model generalization and interpretability. The combined efforts of policy makers, industry stakeholders, and technology experts are essential to make effective use of CNN-based solutions and create safer, healthier, and more sustainable travel experiences.