Air Quality Monitoring Stations Research Articles

Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran

Abstract. Tropospheric ozone time series consist of the effects of various scales of motion, from meso-scales to large timescales, which are often challenging for global models to capture. This study uses two global datasets, namely the reanalysis and the daily forecast of the Copernicus Atmosphere Monitoring Service (CAMS), to assess the capability of these products in presenting ozone's features on regional scales. We obtained 16 relevant meteorological and several pollutant species, such as O3, CO, NOx, etc., from CAMS. Furthermore, we employed a comprehensive set of in situ measurements of ozone at 27 urban stations in Iran for the year 2020. We decomposed the time series into three spectral components, i.e., short (S), medium (M), and long (L) terms. To cope with the scaling issue between the measured data and the CAMS' products, we developed a downscaling approach based on a long short-term memory (LSTM) neural network method which, apart from modeled ozone, also assimilated meteorological quantities as well as lagged O3 observations. Results show the benefit of applying the LSTM method instead of using the original CAMS products for providing O3 over Iran. It is found that lagged O3 observation has a larger contribution than other predictors in improving the LSTM. Compared with the S, the M component shows more associations with observations, e.g., correlation coefficients larger than 0.7 for the S and about 0.95 for the M in both models. The performance of the models varies across cities; for example, the highest error is for areas with high emissions of O3 precursors. The robustness of the results is confirmed by performing an additional downscaling method. This study demonstrates that coarse-scale global model data, such as CAMS, need to be downscaled for regulatory purposes or policy applications at local scales. Our method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.

Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil

Abstract. Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (https://doi.org/10.57760/sciencedb.09858, Hoinaski et al., 2023a; https://doi.org/10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (https://doi.org/10.57760/sciencedb.09857, Hoinaski and Will, 2023a; https://doi.org/10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (https://doi.org/10.57760/sciencedb.09859, Hoinaski and Will, 2023b; https://doi.org/10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​

Assessment of ground-level ozone pollution in Türkiye according to new WHO limits.

Ground-level ozone is a secondary pollutant and is attributable to respiratory diseases and mortality. For this reason, the World Health Organization (WHO) implemented a new long-term (peak season) limit value for ozone. The previous studies related to ozone in Türkiye were spatially limited to certain locations. In this study, annual mean and peak season ozone concentrations, and limit exceedances were investigated for Türkiye for the year 2021. Moreover, ozone peak seasons were determined for the first time for 126 air quality monitoring stations. The annual mean ozone concentration was determined as 44.3 ± 19.3 µg/m3 whereas the peak season average ozone level was 68.4 ± 27.2 µg/m3. April-September period was the most frequently observed ozone peak season. Among all stations, Erzurum Palandöken was by far the most polluted station in terms of annual mean and limit exceedances of ozone. Ankara Siteler stations have the highest rank in peak season mean. 87 and 83 stations exceeded the short-term and long-term recommendations of WHO, respectively. Four hotspot regions were revealed in terms of peak season exceedance: Adana and surrounding provinces, the surroundings of Burdur and Isparta provinces, and the northeastern and northwestern parts of Türkiye. To protect public health, WHO recommendations for 8-h and peak season limits should be immediately implemented in Turkish regulations.

Machine Learning Classification of Air Quality Monitoring Stations to Achieve Ambient NO2 Objectives Using Emission Scenarios and Chemical Transport Model

Exceeding the latest Canadian Ambient Air Quality Standards (CAAQS) for NO2 concentration in Canadian cities motivates discussions on new policies and directives. Advanced air quality modeling and machine learning clustering algorithms integrating the Weather Research Forecast (WRF) and Community Multiscale Air Quality (CMAQ) models were used to classify air quality monitoring (AQM) stations based on pollution concentration modeling results. The sensitivity of ambient NO2 to the primary anthropogenic emission sources was investigated in Alberta, Canada. Emissions from two main sources, upstream oil and gas (UOG) and transportation sources, have been identified as the reason Alberta fails to meet the newly adopted NO2 CAAQS objectives. The air quality model was validated with ground-level observation data and the atmospheric model accurately replicates spatiotemporal NO2 variations. Despite contributing 62% of Alberta’s total NOx emissions, UOG influences ambient NO2 concentrations modestly in urban areas (<10%) but significantly affects rural regions. In contrast, transportation emission sources, responsible for 23% of NOx emissions, dominate ambient NO2 levels (up to 63%) in large cities. The discrepancy of emission contribution and ground-level concentrations, obtained from the chemical transport model, was resolved using a k-prototypes clustering algorithm to propose a new approach for categorizing AQM stations which led to improving the conventional classifications. The new approach considered the sensitivity of NO2 to emission reduction scenarios and provided an improved classification to be used for emission reduction interventions. Based on the updated classification, one set of AQM stations clearly showed sensitivity to NO2 emission reduction in the transportation sector despite their lower contributions to overall emissions. These stations were categorized as population exposure stations in large cities.

Deep learning models for air quality forecasting based on spatiotemporal characteristics of data

Deep learning models for air quality forecasting based on spatiotemporal characteristics of data

Tropospheric Ozone levels in and around southern city Bengaluru, India

The diurnal to seasonal ozone (O3) pre-cursors (NO, NO2, CO, VOCs) trends over local climatic zones (LCZs) with the changes in weather patterns with respect to land cover classes determines the O3 trends and useful for air quality management. In this work, diurnal to seasonal patterns of O3 concentrations are analysed across the different LCZs in Bengaluru region. The Central/State Pollution Control Boards (CPCB/SPCB) operate more than 500 continuous air quality and weather monitoring stations across the India. Hourly O3 concentration data were collected from CPCB for 5 different LCZs within the Bengaluru region over a period of four years (2019-2022). Time series analyses were conducted to examine the temporal variations in O3 levels. The results showed significant fluctuations in diurnal trends of O3 due to the influence of air temperature and relative humidity variations across the five LCZs within Bengaluru region. There were distinct seasonal trends in O3 concentrations with maximum (60-65 ppb) observed during the winter and summer seasons and that of minimum concentration in the range 20-25 ppb during the monsoon months. The observed maximum ozone levels (60 -65 ppb) found to be beyond the threshold national ambient air quality standard (NAAQ) and the accumulation exposure over threshold of 40 ppb (AOT 40) during the winter and summer. The O3 concentration trends are in line with the published research on premature mortality about 1200 annually for Karnataka state for chronic obstructive pulmonary disease owing to population exposure to O3 beyond its threshold level of NAAQ. Also, the annual rice yield loss is about 2% (~0.1 Metric Tonne) with the AOT 40 to paddy crop for Karnataka state. Thus, O3 diurnal to seasonal trends serve the first-hand information to local authorities for the action plan of air quality management taking into consideration O3 precursors, wind speed, and wind direction.

Comprehensive analysis of long-term trends, meteorological influences, and ozone formation sensitivity in the Jakarta Greater Area

Jakarta Greater Area (JGA) has encountered recurrent challenges of air pollution, notably, high ozone levels. We investigate the trends of surface ozone (O3) changes from the air quality monitoring stations and resolve the contribution of meteorological drivers in urban Jakarta (2010–2019) and rural Bogor sites (2017–2019) using stepwise Multi Linear Regression. During 10 years of measurement, 41% of 1-h O3 concentrations exceeded Indonesia’ s national threshold in Jakarta. In Bogor, 0.1% surpassed the threshold during 3 years of available data records. The monthly average of maximum daily 8-h average (MDA8) O3 anomalies exhibited a downward trend at Jakarta sites while increasing at the rural site of Bogor. Meteorological and anthropogenic drivers contribute 30% and 70%, respectively, to the interannual O3 anomalies in Jakarta. Ozone formation sensitivity with satellite demonstrates that a slight decrease in NO2 and an increase in HCHO contributed to declining O3 in Jakarta with 10 years average of HCHO to NO2 ratio (FNR) of 3.7. Conversely, O3 increases in rural areas with a higher FNR of 4.4, likely due to the contribution from the natural emission of O3 precursors and the influence of meteorological factors that magnify the concentration.

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.

Observation and study of consecutive dust storms in the Taklimakan desert from March 16 to 27, 2022, using reanalysis models and lidar

A series of severe dust storms hit the Taklimakan desert between March 16 and 27, 2022, significantly deteriorating air quality throughout China. This study presents a comprehensive analysis of the vertical structure of aerosols during these dust storms, as well as their causes and impacts on China's regional and city-scale air quality, utilizing data from reanalysis models, coherent Doppler wind lidar, and air quality monitoring stations. During dust storms, intense wind layers increase dust emissions, resulting in reduced lidar detection ranges and signal strengths. In addition, lower temperatures prevail due to sunlight absorption and scattering. Moreover, high-speed winds at high altitudes increase dust particles that serve as cloud condensation nuclei, leading to increased humidity, decreased temperature, and precipitation. The Taklimakan desert's trough serves as a wind convergence zone, which promotes favorable conditions for the initiation of dust storms. Due to the steep pressure gradients, strong winds enter the Taklimakan desert through the gap between its mountains in the east, coming from northern Xinjiang and Inner Mongolia via the Hexi corridor, which facilitates the lifting and transport of dust aerosols. Dust is transported long distances from the Taklimakan desert to the eastern coasts, impacting numerous cities along the way. The emissions from these dust storms swept across most Chinese provinces. Even though each dust storm was over in the Taklimakan desert, its effects on China's air quality continued for several days. The coarse PM concentrations (PM10–2.5) in Hotan, Kashgar, Aksu, Korla, Hami, Xining, Yinchuan, Taiyuan, and Beijing spiked to levels around 14, 36, 11, 6, 7, 12, 11, 12, and 6 times higher than their 2022 averages, respectively. This study provides valuable insights into the causes and effects of the Taklimakan desert dust storms, helping authorities develop effective mitigation plans.

Short‐term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling

AbstractAir pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short‐term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO2) and particulate matter (PM10), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey–Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short‐term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region.

Air Pollution and the Effects on House Prices: A Push for Sustainability

Environmental sustainability has become an important issue of concern throughout many countries. Among the many sources of concern include air pollution, the effects of which are often capitalized into house prices. In this article, we focus on air pollution in the city of Changsha, China, and the impacts of new pollution regulations on the relationship between pollution exposure and house sales prices. We use the Air Quality Index (Aqi) data from 10 air quality monitoring stations in Changsha to interpolate a previous 12-month average Aqi value for each dwelling unit in the city, based on geographical location. Controlling for important house characteristics such as the living area and the distance to the nearest subway stations, we use the Blue Sky Protection Campaign action plan (BSPC) as the quasi-natural experiment to identify the causal effect of air pollution on house prices. We find dwelling units that were previously in relatively higher-polluted neighborhoods saw their values increase by about 2% after the pollution regulation was implemented, which is statistically significant. This implies China could achieve greater housing wealth for its homeowners by implementing further pollution restrictions, both in Changsha and perhaps also in other parts of the country. Other Asian countries could potentially learn from the results of this analysis.

Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants

Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants

Trans-Boundary Dust Transport of Dust Storms in Northern China: A Study Utilizing Ground-Based Lidar Network and CALIPSO Satellite

During 14–16 March 2021, a large-scale dust storm event occurred in the northern region of China, and it was considered the most intense event in the past decade. This study employs observation data for PM2.5 and PM10 from the air quality monitoring station, the HYSPLIT model, ground-based polarized Lidar networks, AGRI payload data from Fengyun satellites and CALIPSO satellite Lidar data to jointly explore and scrutinize the three-dimensional spatial and temporal characteristics of aerosol transport. Firstly, by integrating meteorological data for PM2.5 and PM10, the air quality is assessed across six stations within the Lidar network during the dust storm. Secondly, employing a backward trajectory tracking model, the study elucidates sources of dust at the Lidar network sites. Thirdly, deploying a newly devised portable infrared 1064 nm Lidar and a pulsed 532 nm Lidar, a ground-based Lidar observation network is established for vertical probing of transboundary dust transport within the observed region. Finally, by incorporating cloud imagery from Fengyun satellites and CALIPSO satellite Lidar data, this study revealed the classification of dust and the height distribution of dust layers at pertinent sites within the Lidar observation network. The findings affirm that the eastward movement and southward compression of the intensifying Mongolian cyclone led to severe dust storm weather in western and southern Mongolia, as well as Inner Mongolia, further transporting dust into northern, northwestern, and northeastern parts of China. This dust event wielded a substantial impact on a broad expanse in northern China, manifesting in localized dust storms in Inner Mongolia, Beijing, Gansu, and surrounding areas. In essence, the dust emanated from the deserts in Mongolia and northwest China, encompassing both deserts and the Gobi region. The amalgamation of ground-based and spaceborne Lidar observations conclusively establishes that the distribution height of dust in the source region ranged from 3 to 5 km. Influenced by high-pressure systems, the protracted transport of dust over extensive distances prompted a gradual reduction in its distribution height owing to sedimentation. The comprehensive analysis of pertinent research data and information collectively affirms the precision and efficacy of the three-dimensional aerosol monitoring conducted by the ground-based Lidar network within the region.

Dynamic Neural Assimilation: a deep learning and data assimilation model for air quality predictions

Ambient air pollution is known to be a serious issue that has an impact on human health and the environment. Assessing air quality is of the utmost importance to protect human health and the environment. Different tools are available, from monitoring stations to complex models. These systems are capable of accurately predicting air quality levels, but they are often computationally very expensive which makes them poorly efficient. In this paper, we developed a novel model called Dynamic Neural Assimilation (DyNA) integrating Recurrent Neural Networks and Data Assimilation methods to derive a physics-informed system capable of accurately forecasting air pollution tendencies and investigating the relationship with industrial statistics. DyNA is trained in historical data and is fine-tuned as soon as new data comes available. We trained and tested the system on real data provided by the air quality monitoring stations located in Italy from the European Environment Agency and simulated results derived from the air quality modelling system Atmospheric Modelling System-Model to support the International Negotiation on atmospheric pollution on a National Italian level. We analysed air pollution data in Italy from the years 2003–2010 and studied its correlation with nearby industries in some regions where monitoring sensors were available.

Mapping nationwide concentrations of sulfate and nitrate in ambient PM2.5 in South Korea using machine learning with ground observation data

Particulate matter (PM) is a major air pollutant in Northeast Asia, with frequent high PM episodes. To investigate the nationwide spatial distribution maps of PM2.5 and secondary inorganic aerosols in South Korea, prediction models for mapping SO42− and NO3− concentrations in PM2.5 were developed using machine learning with ground-based observation data. Specifically, the random forest algorithm was used in this study to predict the SO42− and NO3− concentrations at 548 air quality monitoring stations located within the representative radii of eight intensive air quality monitoring stations. The average concentrations of PM2.5, SO42−, and NO3− across the entire nation were 17.2 ± 2.8, 3.0 ± 0.6, and 3.4 ± 1.2 μg/m3, respectively. The spatial distributions of SO42− and NO3− concentrations in 2021 revealed elevated concentrations in both the western and central regions of South Korea. This result suggests that SO42− concentrations were primarily influenced by industrial activities rather than vehicle emissions, whereas NO3− concentrations were more associated with vehicle emissions. During a high PM2.5 event (November 19–21, 2021), the concentration of SO42− was primarily influenced by SOX emissions from China, while the concentration of NO3− was affected by NOX emissions from both China and Korea. The methodology developed in this study can be used to explore the chemical characteristics of PM2.5 with high spatiotemporal resolution. It can also provide valuable insights for the nationwide mitigation of secondary PM2.5 pollution.

Health and economic burden avoided due to air pollution reduction during the COVID-19 Lockdown: Portugal case study

Since the onset of the COVID-19 pandemic, strict mobility measures and lockdown periods were implemented globally to curtail the spread of the virus. Thus, this study aimed to estimate the health and economic burden avoided due to the air pollution reduction during the COVID-19 lockdown in Portugal. PM10, NO2, and O3 hourly concentrations were obtained from Portugal's 68 air quality monitoring stations for the 2020 lockdown and historical (2015–2019) periods to estimate the avoided premature deaths, avoided asthma-related morbidity and avoided hospital admissions due to respiratory diseases, and related avoided costs. Overall, a higher number of avoided premature deaths was estimated, especially related to short-term NO2 exposure (less 30 %). Moreover, the highest avoided morbidity effects were estimated for the short-term NO2 exposure on hospital admissions due to respiratory diseases (57 % of avoided admissions). Economically, €48.3 million were avoided from the averted NO2-related premature deaths, and up to 0.1 % of the total health expenditure was saved due to the avoided new asthma cases in children aged 5–19 years old. In sum, the air pollution reduction during the 2020 lockdown relative to the historical period led to human health gains and related avoided economic costs.

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations.The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

Assessment of Air Pollution by Aerosols over a Coal Open-Mine Influenced Region in Southwestern Romania

The mining activity in open-pit coal sites exerts a high pressure over the surrounding environment and on the health of people working and living nearby. Using data form a total of six field campaigns, this study aimed to capture the level of exploitation activities in the residential area close to the coal open-mine in terms of mass concentrations and total carbon fractions (TC) of ambient PM10, PM2.5 samples. Sampling campaigns were performed between spring of 2018 and spring of 2020, covered both working (full time, part-time) and non-working days at the coal exploitation, and captured also the seasonal variations of different fractions of PM and of their carbonaceous fraction. Fine fraction was also analyzed based on the aerosol Angstrom exponent, determined from measurements of the aerosol scattering coefficient at 450 nm, 550 nm and 700 nm by a nephelometer. The mass concentrations of PM and TC showed significant variations from full time working days to non-working days. The air quality (AQ) level is determined by the air quality index (AQI) specific for PM10. In situ measurements clearly indicated that AQIlocal gives better image on the level of air pollution than the AQIestim from AQ monitoring stations in the region, closest to the Matasari site. Insights into the morphology and composition of particles were obtained by scanning electron microscopy (SEM) and energy dispersive X-ray (EDX/EDS) and Fourier transform infrared spectroscopy (FTIR) analyses. Major elements that were identified C, O, Si, Ca, K, S, Cu, Ni, Fe, Mg, Ti have both crustal and anthropogenic origin, in various proportions.

Evaluation of the Effectiveness of Feature Selection Methods Combined with Regression Algorithms to Predict Particulate Matter (PM10) in Gandhinagar, Gujarat, India

Feature selection is one of the important data pre-processing techniques that are used to increase the performance of machine learning models, to build faster and more cost-effective algorithms, and to make it easier to interpret the predictions made by the models. The main objective of this research work is to investigate the influence features to predict particulate matter (PM10). This research uses 24-hour average pollutant concentration data of 36 air quality monitoring stations provided by Gandhinagar Smart City Development Limited (GSCDL), Gandhinagar, Gujarat. Important features were identified using five feature selection techniques (correlation, forward selection, backward elimination, Exhaustive Feature Selection (EFS), and feature importance derived using Random Forest Regressor). With selected features six regression algorithms (Multiple Linear Regression, Random Forest, Decision Tree, K-nearest Neighbour, XGBoost, and Support Vector Regressor) were trained to predict PM10. Further, the models were compared based on the Root Mean Square Error (RMSE) and Coefficient of determination (R2) parameters to identify the model with good performance. This proposed model can be utilized as an early warning system, providing air quality information to local authorities to develop air-quality improvement initiatives.

Investigation of 2021 wildfire impacts on air quality in southwestern Turkey

In the summer of 2021, unusually intense wildfires happened in southwestern Turkey, especially in Antalya and Mugla with destroying effects on forests, wildlife, and communities residing there. This study aims to understand the air quality impacts of these wildfires. A time period (June–September 2021) covering pre-, fire, and post-periods was investigated using NO2 ground-based observations and TROPOMI CO, HCHO, and NO2 satellite retrievals along with VIIRS FRP. Eight fire regions were selected for detailed analysis in Antalya (An-1-2), Mugla (Mu-1-4), and Mersin (Me-1-2). The highest fire intensity was found in An-1 and followed by Mu-1. CO also showed strongest signals over An-1 (6.73 × 1018 molecules/cm2) with highest levels (1.44 × 1019 molecules/cm2) in the downwind of the wildfires. NO2 showed fire signals in or in very close proximity to the fire regions with strongest signal and largest impact area in An-1 (>8.75 × 1016 molecules/cm2). HCHO showed a different pattern due to HCHO undergoing chemical production and loss in the wildfire plume. HCHO highest levels were not observed over the fire regions, but inside the transported plume with maximum levels for An-1 (3.60 × 1016 molecules/cm2) and for An-2 (3.23 × 1016 molecules/cm2). CO and NO2 increase continued not only in fire, but also in post-fire period, whereas HCHO levels indicated decreases in post-fire compared to pre-fire period. The increases in column concentrations in fire period ranges from 17.6 to 123.1% for CO, 40.6–116.5% for HCHO and 34.4–294.5% for NO2. Higher increases were observed over the Mediterranean Sea especially for CO. Correlations with FRP indicated highest correlations with CO and NO2 and low correlations with HCHO. This study showed that the capability of sparse, ground-level air quality monitoring stations to capture wildfire impacts are limited in the region, because the plume transport was driven by wind direction and wildfire smoke plumes are elevated due to buoyancy. Satellite retrievals are better for capturing the wildfire plume transport and estimating the overall air quality impacts of wildfires.