Ambient Air Quality Monitoring Research Articles

Simple and Low-Cost Ground Level Ozone Measurement System Based on Internet of Things

Air quality monitoring in Indonesia is still not optimal owing to the lack of ambient air quality monitoring stations. Air quality in Indonesia is expressed by the air pollution status index (ISPU). Ozone is one of the parameters with the highest intensity at ambient air quality monitoring stations which is one of the parameters of ISPU. This study aims to design an ambient air ozone concentration monitoring device connected to a wireless network using economical components. This system uses the MQ131 sensor, DHT22 sensor, STM32 Blue Pill microcontroller, and Lora module, which is equipped with a power supply and protector, and displays monitoring results that provide an overview and additional information related to the air quality of the monitoring location on the website. The designed monitoring device can record ambient air ozone levels at the monitoring location with an average value of 20.33 μg/m3 with an average percent error of 2.34%. The ISPU value obtained during the measurement was 8, and the ISPU status was categorized as good.

A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow

The detrimental impacts of fine particulate matter (PM2.5) on human health, climate, ecosystems, crops, and building materials are well-established. However, there remain unresolved inquiries regarding the precise location of the sources of PM2.5. This study is the first attempt to use a calibrated sensors-based ambient air quality monitoring network (SAAQM network) and regulatory government monitors to train micro-satellite images for high spatial-resolution air pollution field determination of PM2.5 in Lucknow, Uttar Pradesh, India. A hybrid approach is developed to integrate three different datasets that include microsatellite images, PM2.5 ground measurements, and supporting information (meteorological parameters and geographical coordinates), to be fed into a Random Trees-Random Forest- Convolutional Neural Network (RT-RF-CNN) joint model to estimate PM2.5 concentrations at a sub-km level. The RT-RF-CNN joint model can derive PM2.5 concentrations at a spatial resolution of 500 m with statistically significant indicators such as spatial r of 0.9, a low root-mean-square error of 26.9 μg/m3 and a mean absolute error of 17.2 μg/m3. Based on our approach, the PM2.5 prediction maps using micro-satellite images (spatial resolution of 3m/pixel) and RT-RF-CNN joint model were generated for each day throughout the study period (December 2021–December 2022). The inter-grid comparison of these maps revealed the intra-urban local hotspots and coolspots at a fine-granular level seasonally, monthly, and daily. It is observed that the monsoon season has the highest number of coolspots (67%), while winter (0.1%), post-monsoon (0.5%) and summer (11%) have fewer. It is noted that the high temporal-spatial information of PM2.5 estimates from our integrated approach is not achievable by ground-based measurements and other existing satellite-based estimates alone. The findings of this study have potential applications on a diverse array, encompassing near real-time daily PM2.5 predicted maps, specific air pollution hotspot identification, PM2.5 exposure assessment at the neighbourhood level, and integration of remote sensing-based micro-satellite images and ground-based measurements.

Evaluating the efficacy of machine learning in calibrating low-cost sensors

Ambient air quality monitoring requires low-cost environmental sensor devices that are affordable and feasible for large-scale implementation. However, issues such as sensor drift, environmental sensitivity, and inter-sensor variability affect data accuracy and cannot be adequately addressed by traditional calibration methods. This paper summarizes the use of machine learning techniques for calibrating low-cost sensors. The literature review shows that machine learning models like Random Forest, Support Vector Regression, and Neural Networks significantly improve sensor accuracy and reliability. For instance, Random Forest models reduced the root mean squared error by 30% for PM2.5 measurements, while Neural Networks achieved an R value of 0.997 for methane sensors. Integrating machine learning with IoT and mobile technologies enhances real-time monitoring and spatial resolution. Identified gaps include the quality of training datasets, managing environmental variability, and improving model transferability across different contexts. Addressing these gaps through advanced models and real-time calibration methodologies will further enhance sensor performance, ensuring more precise and reliable environmental data.

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.

Addressing gaps and disparities in ambient air quality monitoring in the US

Addressing gaps and disparities in ambient air quality monitoring in the US

Prediction of land surface temperature using spectral indices, air pollutants, and urbanization parameters for Hyderabad city of India using six machine learning approaches

Urban areas face rising land surface temperatures (LST) and increased air pollution due to urbanization and industrialization. The consistent rise in LST impacts lives of the residents. Thus, LST forecasts can help manage activities more comfortably. The present study aims to predict LST for Hyderabad city, India using five-year (2018–2022) data on air pollution and meteorological parameters (from ambient air quality monitoring stations) and MODIS LST data. Six machine learning models i.e., Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), XGBoost, and Long Short-Term Memory (LSTM), were used for LST prediction. Considerable influence of PM2.5 and CO (during summer) and SO2 (during winter) on LST was observed which demonstrated high sensitivity of these parameters on LST. LST exhibited a weak correlation with individual air pollutants while strong relationship of LST with all the study variables, when considered simultaneously, was observed. ANN method demonstrated better accuracy with lower error metrics, comprising of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE), compared to the other approaches with ranking in the order ANN > RF > SVR > XGBoost > LSTM > MLR. ANN model for Hyderabad city was validated by using it for prediction of LST of another geographical area i.e. Bengaluru city, India. The result of this study can provide insights for policymakers, urban planners, and environmental agencies for targeted interventions for temperature regulations and to mitigate urbanization's impact.

Fine particulate matter air pollution and health implications for Nairobi, Kenya

Background: Continuous ambient air quality monitoring in Kenya has been limited, resulting in a sparse data base on the health impacts of air pollution for the country. We have operated a centrally located monitor in Nairobi for measuring fine particulate matter (PM2.5), the pollutant that has demonstrated impact on health. Here, we describe the temporal levels and trends in PM2.5 data for Nairobi and evaluate associated health implications. Methods: We used a centrally located reference sensor, the beta attenuation monitor (BAM-1022), to measure hourly PM2.5 concentrations over a 3-year period (21 August 2019 to 20 August 2022). We used, at minimum, 75% of the daily hourly concentration to represent the 24-hour concentrations for a given calendar day. To estimate the deaths attributable to air pollution, we used the World Health Organization (WHO) AirQ+ tool with input as PM2.5 concentration data, local mortality statistics, and population sizes. Results: The daily (24-hour) mean (±SEM) PM2.5 concentration was 19. 2 ± 0.6 (µg/m3). Pollutant levels were lowest at 03:00 and, peaked at 20:00. Sundays had the lowest daily concentrations, which increased on Mondays and remained high through Saturdays. By season, the pollutant concentrations were lowest in April and highest in August. The mean annual concentration was 18.4 ± 7.1 (µg/m3), which was estimated to lead to between 400 and 1,400 premature deaths of the city’s population in 2021 hence contributing 5%–8% of the 17,432 adult deaths excluding accidents when referenced to WHO recommended 2021 air quality guideline for annual thresholds of 5 µg/m3. Conclusion: Fine particulate matter air pollution in Nairobi showed daily, day-of-week, and seasonal fluctuations consistent with the anthropogenic source mix, particularly from motor vehicles. The long-term population exposure to PM2.5 was 3.7 times higher than the WHO annual guideline of 5 µg/m3 and estimated to lead to a substantial burden of attributable deaths. An updated regulation targeting measures to reduce vehicular emissions is recommended.

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.

Assessment of the Ambient Air Quality of Sangli City, Maharashtra, vis-à-vis National Ambient Air Quality Standards (NAAQS)

Assessment of ambient air quality is important to formulate short-term and long-term plans to mitigate air pollution. This study attempts to present an ambient air quality assessment for Sangli City, located at 16.85° N, and 74.58° E. The assessment is done through ambient air quality data measured over three ambient air quality monitoring stations located in residential, commercial, and industrial zones of Sangli City. The data was collected from 2009 to 2017 under the National Air Quality Monitoring Program of the Central Pollution Control Board (CPCB), India. The observed concentrations of gaseous pollutants, SO2 and NOx, and respirable particulate matter are compared with the concentration levels prescribed by the National Ambient Air Quality Standards (NAAQS) in 2009. The concentration levels of particulate matter are quite on the higher side of the prescribed standards. The levels of NOx are almost approaching the prescribed values, whereas SO2 levels are well within the margin. We have also studied seasonal variations in the pollutants during the study period. In general, the air quality in Sangli City is quite undesirable during the mid-winter to mid-summer period. The study is useful for common men to increase awareness. It also forms baseline data and is useful for local and central government to design appropriate strategies to apply corrective measures, protect health, and promote environmental protection.

Using Diverse Data Sources to Impute Missing Air Quality Data Collected in a Resource-Limited Setting

The sustainable operation of ambient air quality monitoring stations in developing countries is not always possible. Intermittent failures and breakdowns at air quality monitoring stations often affect the continuous measurement of data as required. These failures and breakdowns result in missing data. This study aimed to impute NO2, SO2, O3, and PM 10 to produce complete data sets of daily average exposures from 2010 to 2017. Models were built for (a) an individual pollutant at a monitoring station, (b) a combined model for the same pollutant from different stations, and (c) a data set with all the pollutants from all the monitoring stations. This study sought to evaluate the efficacy of the Multiple Imputation by Chain Equations (MICE) algorithm in successfully imputing air quality data that are missing at random. The application of classification and regression trees (CART) analysis using the MICE package in the R statistical programming language was compared with the predictive mean matching (PMM) method. The CART method performed better, with the pooled R-squared statistics of the imputed data ranging from 0.3 to 0.7, compared to a range of 0.02 to 0.25 for PMM. The MICE algorithm successfully resolved the incompleteness of the data. It was concluded that the CART method produced better reliable data than the PMM method. However, in this study, the pooled R2 values were accurate for NO2, but not so much for other pollutants.

Preliminary Indications on an Atmospheric Particulate Matter and Rainwater Chemistry in Colombo, Sri Lanka: A Study During Southwestern Monsoon

The atmospheric cleansing nature of rainfall, specifically in the removal of particulate matter, is a determinant of rainwater quality. This study investigated the relationship between atmospheric particulate matter (PM10) concentration and rainwater quality in Colombo during the southwest monsoon period in 2023. The rainwater samples were collected using a manually prepared high- density polyethylene collector and analysed for pH, electrical conductivity (EC), water-soluble cations (Na+, K+, Ca2+, Mg2+, and NH4+), and anions (Cl-, NO3–, and SO42–). The concentration of atmospheric PM10 was obtained from the ambient air quality monitoring station (AQMS) at Battaramulla. The measured data were analysed using multivariate statistical techniques, including principal component analysis and Pearson correlation analysis, to identify relationships between the concentration of atmospheric PM10 and rainwater quality. The atmospheric PM10 showed an insignificant positive correlation with southwest monsoon rainfall (r=0.14, p>0.05). The concentration of atmospheric PM10 increased the total concentration of water-soluble ions (r=0.30) and decreased the pH (r=-0.42) and electrical conductivity (r=0.07) of rainwater samples. The concentration of Na+, K+ and Ca2+ displayed moderate positive correlation, while Mg2+, NH4+, Cl-,
 NO3- and SO42- showed a weak positive correlation of rainwater samples in the atmospheric PM10.
 This study contributes valuable insights into the variations of PM10 in the atmosphere and its potential implications on rainwater quality in southwest monsoon, underscoring the importance of comprehensive analyses for a more nuanced understanding of the intricate relationships between atmospheric components and rainwater chemistry.
 
 Keywords: Atmospheric chemistry, Correlation analysis, Particulate matter, Rainwater quality, Southwest monsoon

Ambient air quality monitoring of Chandrapur District, Central India

Declining air quality is highly ignored and very common form of degradation of the environment in nations that are both developed and developing. There are several contaminants in the air that have been identified in various studies on air pollution. The crucial parameters of all air pollutants are gaseous and particle pollution. The present study was undertaken to estimate the quality of ambient air in Chandrapur district, Maharashtra state of India. In this paper, an effort has been made to study the standing and trend of Sulphur dioxide (SO2), Oxides of Nitrogen (NOx), Carbon Monoxide (CO), Ozone (O3), Ammonia (NH3), Respirable Suspended Particulate Matter i.e.; PM10, PM2.5, toxic pollutants i.e.; lead, arsenic, nickel, benzo [a] pyarene and benzene and hydrocarbons. The results clearly show that, all parameters of ambient air quality monitoring values were some shown slightly below permissible limit. The ambient air quality monitoring data show that the Chandrapur district has substantial air pollution concerns in terms of SO2, NOx, PM, and other air pollutants. However, after comparing these values with NAAQS levels, the yearly averages of these air pollutants have been found to be below than the NAAQS levels. However, without effective mitigation measures, the concentrations of these pollutants will rise at quite alarming rate.

Comprehensive Hygienic Assessment of Anthropogenic Load on the Territory of the Voronezh Region

Introduction: A comprehensive assessment of adverse human health effects of environmental pollutants found in residential areas has been carried out in the Voronezh Region. Objective: To conduct a comprehensive hygienic assessment of the anthropogenic load on the territory of the Voronezh Region. Materials and methods: We analyzed the results of ambient air, drinking water, and soil quality monitoring collected by the Center for Hygiene and Epidemiology in the Voronezh Region in 2018–2022 for compliance with the maximum permissible concentrations specified in Regulations SanPiN 1.2.3685–21. The complex load was estimated in accordance with method guidelines MR 01-19/17-17. Results: We established that pollution of ambient air in residential areas contributed the most (69.8 %) to the total anthropogenic load in the town Pavlovsk; drinking water – to that in the Semiluksky district (36.5 %), and soil – in the Rossoshansky district (10.5 %). In the city of Voronezh, the level of complex anthropogenic load exceeded both the regional and district averages. The problem of ambient air pollution dominates in all districts; it should be noted, however, that air sampling is carried out only in urban areas and urban-type settlements. In rural areas, effects of agriculture-associated nitrate pollution of drinking water is more pronounced. The impact of soil pollution is more pronounced in urban residential areas. At the same time, the excess of maximum permissible concentrations of soil pollutants were registered neither in rural nor in urban areas over the study period. Conclusion: In Voronezh, the level of complex anthropogenic load exceeds the average values estimated for the region and districts. Ambient air pollution with vehicle and industrial emissions make the greatest contributions to this load both on the territory of Voronezh and in the districts. Nitrate pollution of potable water associated with agriculture is a challenge in rural areas.

Experimental Analysis on Continuous Ambient Air Quality Monitoring System (CAAQMS) at Vandalur, Chennai, India

Experimental Analysis on Continuous Ambient Air Quality Monitoring System (CAAQMS) at Vandalur, Chennai, India

Modulatory effects of dietary saturated fatty acids on platelet mitochondrial function following short-term exposure to ambient Particulate Matter (PM2.5)

ABSTRACT Exposure to ambient fine particulate matter (PM2.5) was found to produce vascular injury, possibly by activating platelets within days after exposure. The aim of this study was to investigate the modulatory effects of dietary saturated fatty acids on platelet mitochondrial respiratory parameters following short-term inhalational exposure to PM2.5. A total of 22 healthy male volunteers were recruited from the Research Triangle area of North Carolina. Platelets were isolated from fresh whole blood samples and mitochondrial respiratory parameters were measured using an extracellular flux analyzer. Intake of saturated fat was averaged from multiple 24-hr dietary recalls. Daily ambient PM2.5 concentrations were obtained from ambient air quality monitoring stations. Correlation and ANOVA were used in data analyses, along with the pick-a-point method and the Johnson-Neyman technique for probing moderation. After controlling for age and omega-3 index, the intake of dietary saturated fatty acids after reaching 9.3% or higher of the total caloric intake significantly moderated the associations between PM2.5 exposure and several platelet mitochondrial respiratory parameters. In conclusion, dietary saturated fatty acids above 9.3% of total caloric intake influenced the relationship between short-term PM2.5 exposure and platelet mitochondrial respiration. Further research is needed to understand these associations and their implications for cardiovascular health.

Ambient Air Quality Monitoring with Reference to Particulate Matter (PM10) in Kolhapur City

Air is an important medium for all living beings and is essential for the well-being of all. Monitoring of air is important to know the quality of air. The air quality monitoring was carried out in Kolhapur City under the National Air Monitoring Program. The present study involves the assessment of PM10 as described in the National Ambient Air Quality Standards (NAAQS). The source apportionment study related to particulate matter was carried out in Kolhapur City. The study also determined the average PM10 concentration in the city as it will be useful for preparing an action plan to reduce PM10 concentration. PM10 concentration was calculated as per the standard method adopted by CPCB. Sampling was carried out for 8 hours in three shifts twice a week at each sampling site for three consecutive years. Mahadwar Road (MR) and Dabholkar Corner (DC) were selected per the surrounding residential area, population density, and traffic conjunction. The third site Shivaji University (SUK), was selected as a control site. The results indicated that the PM10 level has risen above the prescribed standards of NAAQS. The reason for the rise in PM10 may be due to fossil fuel burning, construction activity, vehicles, and unpaved roads. The Analysis of Variance (ANOVA) technique is used to check the equality of the mean concentration of PM10 at these three locations and found a significant difference between mean concentrations of PM10, suggesting increased particulate matter.

PROFOUND INVESTIGATION OF DUST CHEMICAL COMPONENT IN INDUSTRIAL EMISSIONS FOR CORRECT ESTIMATION OF GEOECOLOGICAL IMPACT AND MONITORING

Background. Effective management of dust emissions from industrial enterprises should be based on reliable data on emission sources and emitted components. Correct estimation of geoecological impacts exerted by dust emissions is a complicated process due to chemical diversity of the soil component in them.
 Aim. The aim was to test a practical approach to identifying actual ambient air pollution with solid particles in a zone influenced by an economic entity for correct estimation of impacts and monitoring of ambient air quality.
 Materials and methods. Samples of industrial dust emissions were analyzed with a scanning electronic high-resolution microscope (magnification was from x5 to x300,000; acceleration voltage varied between 0.3 to 30 kW) with S3400N HITACHI x-ray fluorescent device (the limit of detection was approximately 5–10 mass % and the minimum field of view was 100 µm). X-ray photographs were processed by using XRD 6000/7000 software, Version 5.21. The results obtained by calculating dispersion of the dust component were mapped with ArcView 3.2 and ArcGIS 9.3.1., GIS-platforms created by ESRI.
 Results. Complex investigations of dust emissions were performed at a selected enterprise and this made it possible to identify a profile of the dust component in emissions from major sources. Actual composition of dusts emitted by the analyzed enterprise was established to differ fundamentally from the data declared in the inventory lists of emissions issued by the enterprise. The results of dispersion calculations gave grounds for spotting out substances that were priority (marker) ones for the analyzed production and we also estimated actual effects produced by dust emissions on ambient air.
 Conclusions. The study results allow concluding that a dust emission profile is a reliable instrument for estimating actual impacts of an industrial enterprise on ambient air. It can be applied to solve tasks within setting standards, monitoring and estimating actual impacts exerted by anthropogenic dust emissions when environmental measures are being planned and developed.

Long-Term Assessment of PurpleAir Low-Cost Sensor for PM2.5 in California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. Recent advances in air quality monitoring have produced portable, easy-to-use, low-cost, sensor-based monitors which have given a new dimension to air pollutant monitoring and have democratized the air quality monitoring process by making monitors and results directly available at the community level. This study used PurpleAir © sensors for PM2.5 assessment in California, USA. The evaluation of PM2.5 from sensors included Quality Assurance and quality control (QA/QC) procedures, assessment concerning reference-monitored PM2.5 concentrations, and the formulation of a decision support system integrating these observations using geostatistical techniques. The hourly and daily average observed PM2.5 concentrations from PurpleAir monitors followed the trends of observed PM2.5 at regulatory monitors. PurpleAir monitors also captured the peak PM2.5 concentrations due to incidents such as forest fires. In comparison with reference-monitored PM2.5 levels, it was found that PurpleAir PM2.5 concentrations were mostly higher. The most important reason for PurpleAir’s higher PM2.5 concentrations was the inclusion of moisture or water vapor as an aerosol in contrast to measurements of PM2.5 excluding water content in FEM/FRM and non-FEM/FRM monitors. Long-term assessment (2016–2023) revealed that R2 values were between 0.54 and 0.86 for selected collocated PurpleAir sensors and regulatory monitors for hourly PM2.5 concentrations. Past research studies that were conducted for mostly shorter periods resulted in higher R2 values between 0.80 and 0.98. This study aims to provide reasonable estimations of PM2.5 concentrations with high spatiotemporal resolutions based on statistical models using PurpleAir measurements. The methods of Kriging and IDW, geostatistical interpolation techniques, showed similar spatio-temporal patterns. Overall, this study revealed that low-cost, sensor-based PurpleAir sensors could be effective and reliable tools for episodic and long-term ambient air quality monitoring and developing mitigation strategies.

Long term trend analysis and Spatio-temporal variation of air pollutants over Uttar Pradesh, India

Air pollution poses a significant challenge for developing countries like India, particularly in cities where industrial growth and urban development have led to increased levels of air pollutants. Due to limited monitoring resources and a focus on specific variables, there is a lack of comprehensive understanding regarding air quality in Indian urban areas. This study aims to explore the spatial and temporal variations in air pollutant concentrations and identify long-term trends over a seven-year period (2016-2022) in the state of Uttar Pradesh. Data for PM10, SO2, and NO2 were collected from 78 ambient air quality monitoring stations. The study investigates how these pollutants behave in terms of their distribution on different temporal scales (seasonal, yearly) as well as across various locations. The findings reveal that the concentration of PM10 consistently exceeds national and international air quality standards. Using the Inverse Distance Weighting (IDW) technique, air pollutant distribution maps were created to identify specific hotspots. Additionally, the Mann-Kendall Test was applied to analyze trends within the available data. This research can serve as valuable information for local government authorities to formulate effective strategies for mitigating air pollution in the region.

Ambient air quality monitoring and assessment of an industrial area as a tool for sustainable environmental management

Different ambient air pollutants like PM10, PM2.5, SO2 and NO2 were studied for three years with respect to seasons starting from Mar’2017 to Feb’2020 at eight different monitoring locations (ML). It has been revealed from the study that, the levels of air pollution are worsening year by year. It is due to increased transportation of raw materials and finished products from industries and mining activities near to the study area. During the study period 2020–21, the maximum concentrations of PM10 was observed in winter season at ML-5, ML-3 i.e. 216.5 μg/m3, 212.3 μg/m3 and PM2.5 at ML-3, ML-2 i.e. 111.8 μg/m3, 100.4 μg/m3 respectively followed by summer season which exceeding the standard norms. However, it was observed from the study that, in general during all the three seasons and years the concentrations of SO2 & NO2 and found well within the norms. Wind rose graph during October to December 2019 shows the wind direction (predominant) was NW to SE and the wind class frequency distribution graph shows that the air is 26.5 % calm. Similarly the wind rose and wind class distribution pattern during March- May 2019 shows that the wind direction (predominant) was SW to NE and the wind class frequency distribution graph shows that the air is 30.2 % calm.