Smog Episodes Research Articles

Analysis of spatial variability of smog episodes over National Capital Delhi during (2013–2017)

Air pollution is a pressing issue in Delhi, with smog occurrences causing reduced visibility and various respiratory problems. A series of severe SMOG (smoke + fog) episodes between 2013 and 2017 with reduced visibility and exceptionally high PM2.5 concentrations have been reported in Delhi especially around Diwali festival (October–November). The Smog of 2016 is referred as Great Smog of Delhi. This study examined remote sensing data from 2013 to 2017 to investigate smog episodes in Delhi during pre-Diwali, post-Diwali, and Diwali. Satellite-derived parameters viz absorbing aerosol index (AAI), aerosol optical depth (AOD), and ozone monitoring instrument (OMI) along with air pollution data and climatic parameters were used to analyze smog episodes. The results showed that during smog episodes, AOD, AAI and PM2.5 concentrations exceeded permissible limits significantly at all stations across Delhi during the Diwali festival. The ground-based observations at different locations across Delhi and satellite data-derived datasets confirmed the severity of smog episodes. The findings indicate that burning of fire crackers coupled with agriculture stubble burning and subsequent transport of the smoke from North Western states through the Capital had a greater impact on deteriorating air quality in Delhi than local pollution, especially during unfavorable weather conditions associated with high humidity and weaker winds. The outcomes highlight the significance of remotely sensed information in identifying smog episodes and their severity in Delhi. It also underlines the necessity for efficient interventions to control air pollution, particularly amid festivals like Diwali.

Solving the mysteries of Lahore smog: the fifth season in the country

South Asian smog is an annually recurring air pollution event that is characterized by high concentrations of air pollutants, low visibility, and severe socio-economic disruptions. It is most frequently observed across the north-western parts of the Indo-Gangetic Plains (IGP). Since 2016, it has become so frequent and pervasive that they are colloquially referred to as “the fifth season” in the region. During this season populations residing in this area including cities such as Lahore, Amritsar, Faisalabad, Multan, Delhi etc. are exposed to hazardous levels of air pollution. This study attempts to understand the reasons for the recent increase in the intensity and frequency of intense air pollution episodes by utilizing ground-based and satellite observations. Time-series analysis, seasonal and annual variations in PM2.5 and AOD were carried out. Satellite data of UVAAI, CO, and fire count were used to determine the fire incidences and aerosol characterization. The results indicate that during the last decade, there has been an increase in air pollution sources while crop residue burning, and motor vehicles have greatly contributed to the increased frequency and intensity of such events. The meteorological and topographical data analysis revealed that the IGP provides ample cloud-condensation nuclei and optimum conditions for the accumulations of pollutants, especially in the post-monsoon and winter periods. The Mann-Kendall test was performed to confirm the annual and seasonal trends of PM2.5. The major cities of South Asia such as Lahore, Delhi, Kathmandu, and Dhaka have recently shown a decreasing trend with respect to annual AOD. However, an increase has been observed for the period of post-monsoon especially for cities of Lahore and Delhi. To distinguish potential sources of air pollutants during extreme smog episodes in the region, Analytical Hierarchy Process (AHP) was conducted to estimate the most contributing factors to the annual smog episodes. According to the data, the transboundary pollution resulting from open-field agriculture fires has been assigned moderate importance over vehicular emissions. A HYSPLIT trajectory model coupled with ground observations and satellite data shows that the agricultural fires do have a profound impact on the air quality of the region. It highlights the importance of transboundary pollution and cooperation among cities, regions, and countries across the shared airshed of the Indo-Gangetic Plains.

Critical Review and Comparative Analysis of the Government of Punjab’s ‘Policy on Controlling Smog, 2017’ with Counterpart Strategies in London, Beijing and Los Angeles

Lahore, the capital of Punjab, Pakistan, has experienced worsening episodes of smog since the early 2000s. Poor air quality reduces the life expectancy of its population by roughly seven years and results in 128,000 premature annual deaths. According to various emissions inventories, Nitrogen Oxides (NOx) and Particulate Matter (PM) frequently exceed World Health Organization (WHO) standards, more than other air pollutants in the city. In 2017, the Government of Punjab developed the ‘Policy on Controlling Smog’ to mitigate the effects of air pollution across Punjab, including Lahore. This paper critically examines the policy. First, it establishes a scientific baseline to describe smog to justify a comparison between Lahore, London, Beijing, and Los Angeles. Second, it establishes a positive correlation between poor air quality and negative public health outcomes. Next, it points out scientific, data and other concerns in Punjab’s smog policy, and then compares it and draws lessons from counterpart policies in the same cities. The paper argues and concludes that while the ‘Policy on Controlling Smog, 2017’ is a useful blueprint to organise efforts in Pakistan’s Punjab province, it is not leveraging lessons from other cities nor is it consistent with the latest scientific research on air quality in general and Lahore in particular. Hence, it is ill-equipped to maximise public health outcomes. JEL Classification Codes: I18

Relative Risks of Cardiopulmonary and Lung Cancer Mortality by PM2.5 Exposure in Ambient Air of Delhi Particularly During Smog Episode

Background Delhi, the capital city of India has experienced the problem of the great smog during November since a long time. Adverse meteorological conditions, stubble burning, and the celebration of Diwali were considered as the major responsible factors for the smog episodes. This study was designed to identify the concentration and relative risks associated with the exposure of PM2.5 in ambient air of Delhi during the episodic events. Methods 24-h average ambient levels of PM2.5 were procured for the year 2015–2020. Relative risks of cardiopulmonary and lung cancer diseases associated with PM2.5 exposure have been estimated using a probabilistic approach of risk assessment. Findings The average relative risks (2015–2020) for lung cancer mortality (RR 2.15; 95% CI 2.13, 2.17., p < 0.001) were observed higher than cardiopulmonary mortality (RR 1.66; 95% CI 1.65, 1.67., p < 0.001). An attempt has been done to identify the effect of high exposure concentration on relative risks particularly during smog period. The estimated relative risks during November (2016–2018) for lung cancer mortality were ranged as 2.51–2.68 and cardiopulmonary mortality were ranged as 1.85–1.93. Conclusion Such high levels of associated relative risks during such episodes could be considered as an important additive hazard factor for overall mortality burden.

INFLUENCE OF POINT SOURCES OF POLLUTION ON AIR QUALITY IN MAOPOLSKA FIRST TESTS OF A NEW VERSION OF FORECASTING OF AIR POLLUTION PROPAGATION SYSTEM

Polluted air is dangerous to human life and health. Particulate matter, among others PM10, isone of the most harmful substances. In Maopolska and its capital Krakw, the concentrations ofharmful substances often exceed the standards set by the World Health Organization. Krakw,thanks to the ban on residential heating with solid fuels, has significantly reduced emissions ofpollution, but they remain high in the remaining part of the region, affecting air quality in thecapital as well. With the frequent occurrence of high concentrations of pollutants, in addition tothe necessary measures aimed at reducing emissions, forecasting of air pollutant concentrations isneeded to inform the population if normative concentrations are likely to be exceeded. The FAPPS(Forecasting of Air Pollution Propagation System), based on the AROME/MM5/CALMET/CALPUFF model ensemble, has been operating in Maopolska since 2014 and has been used tocreate pollution concentration forecasts for Krakw and Maopolska. In this study, the influence ofemissions from point sources on air quality in Maopolska was investigated based on the results ofmodellingwith this system. Modelling results indicate that this influence is negligible. The qualityof PM10 forecasts for four versions of the FAPPS system, differing in the meteorological modelused MM5 (Fifth-Generation Penn State/NCAR Mesoscale Model) or WRF (Weather Researchand Forecasting) and the emission input (2015 emissions from the Maopolska Marshals Office,updated with data from the City of Krakw for 2018, or the 2020 emission inventory from theCentral Emission Database) was tested. The quality of forecasts was assessed based on the resultsof measurements at the 22 air pollution monitoring stations for three smog episodes that occurredon 1117.11.2021, 1115.12.2021 and 1318.03.2022. The best results for Krakw were obtainedusing an approach based on the WRF model and the emission inventory from the Central EmissionDatabase, for which an RMSE (Root Mean Square Error) value of 30.02 g/m3 was obtained forselected episodes. In the case of Maopolska, the lowest RMSE value (33.58 g/m3) was obtainedfor the system using the emission inventory obtained from the Marshals Office and the WRF model. First tests indicate that changing the meteorological model from MM5 to WRF can lead toimproved modelling results, but, further research is needed to confirm it.

Recurring South Asian smog episodes: Call for regional cooperation and improved monitoring

Recurring South Asian smog episodes: Call for regional cooperation and improved monitoring

Air pollution and cardiovascular disease onset: hours, days, or years?

Before the advent of exposure assessment tools for particles and component pollutants, such as particulate matter (PM) or total suspended particles, investigators relied on counting the number of excess deaths before and after episodes of smog to draw inferences. The most well known historical event is the great smog of London in 1952, during which a combination of cold weather, low-quality household coal, and factory emissions resulted in thousands of excess deaths attributable to severe air pollution.

A machine learning approach to investigate the build-up of surface ozone in Mexico-City

Ground-level ozone is an important pollutant regarding air quality and climate. Mexico City frequently experiences severe ozone episodes due to a combination of strong ozone precursor emissions and its specific topographical environment which critically impacts meteorological conditions. High ozone levels during these episodes cause harmful effects to the public health and the environment. This necessitates ranking air quality and meteorological variables according to their contributions towards the build-up of ozone. In this study, three machine learning models are used to learn a prediction function with hourly data of eight predictors as input and hourly ground-level ozone mixing ratios as output. One-year hourly data of eight predictors collected in Mexico-City from March 2015 to February 2016 is employed to train and test the models. The best model, capturing ozone peak levels with 92% accuracy during 6–18 March 2016, is used to rank the predictors according to their importance in the build-up of ozone applying a shapley additive explanations approach based on the game theory shapley values. This 6–18 March 2016 period encompassed different meteorological and emission conditions and included a severe ozone smog episode from 12 to 17 March 2016. Such ranking of the air quality and meteorological variables is crucial for policy-making decisions regarding the prevention and mitigation of ozone detrimental effects during severe ozone episodes and provides insight into the functional dependency of ozone on its predictors. The proposed approach showcases Mexico City, but its principles can be applied for ozone episodes at any other location.

The risk of respiratory incidents in response to the increase of ozone concentration in the ambient air, in the Silesian Voivodeship, in 2016-2017.

Due to the high level of urbanization and industrialization, Silesian Voivodeship remains a region with the poorest quality ambient air, especially in the winter season in which alarm levels are constantly being exceeded. However, in the summertime, there are observed short-term episodes of high ozone concentrations for which their impact on the population health is poorly documented. The aim of the study was to assess the risk of daily respiratory health problems related to an increased pollutants concentration typical for photochemical smog. In the ecological type of study, secondary epidemiological data were used. They were obtained from the National Health Fund (NFZ) in Katowice and included the number of outpatient visits in primary health care and hospitalizations due to respiratory diseases (J00-J99) and selected acute respiratory incidents registered between 01/01/2016 and 31/08/2017 in the Silesian Voivodeship. In the summertime of both years (2016 and 2017), there were observed short-term episodes of photochemical smog in the study region. Obtained results show a significant increase in the risk of outpatient visits due to total respiratory diseases, and also due to acute pharyngitis, acute laryngotracheitis, bronchitis, and asthma in response to the increase in ozone concentration. Similarly, a significant increase in the risk of hospitalization for all respiratory diseases was found, however, it appeared with a delay of at least two to three weeks. In the case of hospitalization due to bronchitis statistically significant risk was observed 2-4 days after the increase in exposure. The occurrence of registered respiratory incidents was confirmed in response to the increase in ozone concentration, characteristic of the summertime in the Silesian Voivodeship.

Prediction of PM2.5 concentrations using soft computing techniques for the megacity Delhi, India

Over the past few years, the concentration of fine particulate matter (PM2.5) in Delhi’s atmosphere has progressively increased, resulting in smog episodes and affecting people’s health. Therefore, accurate and reliable forecasting of PM2.5 concentration is essential to guide effective precautions before and during extreme pollution events. In this work, soft computing techniques, including Artificial Neural Network and Gaussian Process Regression are employed to predict PM2.5 concentrations in Delhi. Four models, namely, multi-layer feed-forward neural network (MLFFNN), General regression neural network, Gaussian process regression with ARD squared exponential kernel (GPARD_sqexp) and Gaussian process regression with ARD rational quadratic kernel (GPARD_rat_quad) are built using meteorological and air quality data corresponding to a two-year period (2015–2016). The results of the study suggested that MLFFNN showed the best prediction performance among the four models, with testing correlation coefficient (R) 0.949, Root mean square error 30.193, Nash–Sutcliffe efficiency index 0.892 and Mean absolute error 18.388. Moreover, sensitivity analysis performed to understand the importance of different input variables reported that PM10, wind speed, air quality index and aerodynamic roughness coefficient (Z0) are the most critical parameters influencing MLFFNN model forecasts. On the whole, the work has demonstrated that the artificial neural network model is more capable of dealing with PM2.5 forecasting in Delhi urban area than the Gaussian process regression model.

Environmental Monitoring of Smog in Lahore Metropolitan Using GIS Technology

Smog, a combination of polluted gasses or smoke with fog, brings a harmful breathing environment that mostly exists during the pre-winter season in Lahore. The smog episodes are prevailing over the city which are getting aggregated by smoky fumes, impurities, and dust particles. Severe smog episodes were observed over Lahore mainly during November and December 2019, resulting in adverse environmental conditions e.g., eye irritation, breathing difficulty, and creating very low visibility in and around Lahore. This study is based on eighty-five sample sites from all over Lahore city, to measure the Particulate Matter during smog season. In this study, the risk zones are demarcated based on land use and land covers during pre-winter seasons. It has been revealed through geospatial technology and the evaluation of weather data that local pollution has a greater contribution towards deteriorating the quality of air as compared to the contributions which are regionally active during the smog season. Spatial dispersion of smog has shown the local industrial and vehicular emissions as the major contributor of smog rather regional diffusion of pollutants.

Public health and economic impact assessment of PM2.5 from open biomass burning over countries in mainland Southeast Asia during the smog episode

PM2.5 is considered the principal air pollutant caused by open biomass burning (OBB) in Mainland Southeast Asia (MSEA) and significant adverse health effects and economic burdens. The goals of this work are to conduct a literature review on relative measures of the effect associated with the exposure to OBB emission in MSEA, to analyze the impact of OBB emission on PM2.5 concentration in MSEA using the dispersion model, and to estimate the public health and economic impacts associated with the exposure to PM2.5 from OBB on regional and national scales using Environmental Benefits Mapping and Analysis Program during a smog episode. This work found that OBB was the largest emission source in MSEA, contributing 57% to the PM2.5 concentrations, with the substantial OBB impact was found in Northern Laos and Eastern Myanmar. As a result, the exposure to PM2.5 from OBB was estimated to contribute a substantial number of deaths regarding all-cause (78,276 cases - 95% CI: 62,915–92,629), respiratory disease (6,716 cases – 95% CI: 3,639–9,482), and cardiovascular disease (1,076 cases – 95% CI: 490-1,615). The economic impact was estimated at 23.28 billion (95% CI: 18.69–27.57), 1.90 billion (95% CI: 0.88–2.40), and 0.44 billion (95% CI: 0.20–0.67) USD, respectively. OBB emission caused the greatest public health and economic impact in Myanmar, while the smallest impact was found in Cambodia. Our study provides policymakers with quantitative estimates of the OBB impacts so that they can develop appropriate control measures at the regional and national levels in MSEA.

Deployment of networked low-cost sensors and comparison to real-time stationary monitors in New Delhi

ABSTRACT Air quality is a global challenge issue, and many regions of the world, such as India, are experiencing daunting challenges. An important aspect is to identify and then control the emissions from major contributing sources. To advance this aspect, this paper describes an air quality network that has been set up in the National Capital Territory of Delhi (NCT-Delhi) to identify major contributing source categories in real-time. The various components include an innovative cloud-based dashboard to compile the data in real-time from a series of PM instruments (Beta Attenuation Monitors (BAM)) and a low-cost sensor network (22 APT- MAXIMA sensors). Furthermore, at one of the locations (urban site), three real-time chemical speciation monitors are installed to provide elemental speciation (30 elements), elemental carbon (EC), and organic carbon (OC) data. PM2.5 concentrations at different sites (urban, industrial, and background) were compared to the BAM measurements over an 8-month period from May 2019 to February 2020; spanning the summer, monsoon, autumn, and winter seasons in Delhi. The APT sensor measurements were well correlated to the BAM measurements, with R2 values ranging between 0.84 and 0.95 for all sites. This validated that the APT-MAXIMA low-cost sensors can be a useful tool for distributed monitoring of PM2.5 levels. The mean PM2.5 concentrations showed a trend with winter (Dec, Jan, Feb: 205.2 ± 95.1 µg m−3) and autumn (Oct, Nov: 171.7 ± 128.3 µg m−3) highs and summer (May, Jun: 64.6 ± 57.2 µg m−3) and monsoon (Jul, Aug, Sep: 27.6 ± 16.7 µg m−3) lows. The bivariate polar plot reveals high PM2.5 levels originated from local/regional combustion sources located east and south-west of the urban site, especially when high PM2.5 episodes are encountered during the festival season and other smog episodes. Implications: Low-cost sensors are useful for distributed monitoring under both low and high pollution conditions. A cloud-based dashboard system provided real-time, remote access to the data and in the visualization of air quality in the entire region. The real-time data availability on the cloud enabled establishing hot-spot regions of air pollution, spatial variation of PM2.5, real-time source apportionment, and health risk estimates to benefit both policy makers, and the general public.

Remote sensing of aerosol properties during intense smog events over Lahore (Pakistan)

In recent years, smog has been one of the main concerns in heavily populated urban areas like Lahore (Pakistan) and Kanpur (India). Atmospheric pollutants like aerosols play an important role in smog. In this paper, aerosol types are identified in smog episodes, based on Aerosol Robotic Network (AERONET) data, during 4-year period i.e., 2015-2018. Aerosols are classified based on fine mode fraction (FMF) and single scattering albedo (SSA). One of the main aerosol types which are abundant in every smog episode is Black Carbon (BC) aerosol while dust is present throughout the year. BC is responsible for radiation imbalance and is considered the main component in climate changes at regional and global levels. Furthermore, time series of aerosol optical depth (AOD) during smog episodes is used to identify the variability of aerosol burden in the atmosphere. Backward trajectories from the HYSPLIT model are used to trace the origin of aerosols during the days of maximum AOD.

Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

Ultra-Light Airborne Measurement System for Investigation of Urban Boundary Layer Dynamics.

Winter smog episodes are a severe problem in many cities around the world. The following two mechanisms are responsible for influencing the level of pollutant concentrations: emission of pollutants from different sources and associated processes leading to formation of secondary aerosols in the atmosphere and meteorology, including advection, which is stimulated by horizontal wind, and convection, which depends on vertical air mass movements associated with boundary layer stability that are determined by vertical temperature and humidity gradients. The aim of the present study was to evaluate the performance of an unmanned aerial vehicle (UAV)-based measurement system developed for investigation of urban boundary layer dynamics. The evaluation was done by comparing the results of temperature, relative humidity, wind speed and particulate matter fraction with aerodynamic diameter below 10 μm (PM10) concentration vertical profiles obtained using this system with two reference meteorological stations: Jagiellonian University Campus (JUC) and radio transmission tower (RTCN), located in the urban area of Krakow city, Southern Poland. The secondary aim of the study was to optimize data processing algorithms improving the response time of UAV sensor measurements during the ascent and descent parts of the flight mission.

Source identification of fine and coarse aerosol during smog episodes in Debrecen, Hungary

In this study, the identification of PM sources during smog episodes in Debrecen is presented. The smog episodes were characterized by the high concentration of PM10, and were linked to a thermal inversion period in November 2011 in Hungary, when an anticyclone occurred across Europe. The sources of PM2.5 and PMcoarse were identified by positive matrix factorization (PMF). Additionally, the source locations were explored by using the conditional probability function (CPF). A numerical weather prediction model (WRF) was also applied to evaluate the air pollution situation. In order to meet the goals of the study, the elemental composition with high (2-h) time resolution, the elemental size distribution, and the planetary boundary layer were examined in an urban background site of downtown Debrecen, Hungary. Our results highlight the importance of biomass burning (39%) and traffic (31%) as the major contributors to PM2.5. The main tracers of these sources (K, Cl, Pb, Zn) appeared mainly in droplet mode (0.5–1 μm). The major sources of PMcoarse fraction were soil (32%), and dust attributed to tramline construction (30%). The combination of PMF and CPF results revealed that the PM10 concentrations were affected by sources within the urban agglomeration of Debrecen (city centre and suburban areas) under the given meteorological conditions. This conclusion is further validated by the fact that the diurnal variation of the identified source contributions presented a distinct pattern that depended strongly on the daily activities of the city’s residents (compared to the usually unstructured diurnals of transported sources).

The role of the media in staging air pollution: The controversy on extreme air pollution along Oxford Street and other debates on poor air quality in London

Mapping the media frames that have both triggered and articulated the mobilisation around air pollution in London in the period 1997–2017, this paper shows how especially those events that have been framed in agonistic terms have led to a rise of media engagement with the topic. From the controversy around Sahara dust as a so-called ‘natural’ explanation for smog episodes, to the staging of Oxford Street as the most polluted street of the world, from Sadiq Khan's decision to join ClientEarth in its court case against the UK government to the debate on a new runway for Heathrow, from the Volkswagen scandal to the Black Lives Matter blockade of London City Airport: the discursive construction of us/them divides has been crucial in sparking passion, contestation and debate. Empirically, the paper starts from a detailed study of 1594 newspaper articles that appeared on air pollution in five British newspapers, The Guardian, Financial Times, The Independent, The Daily Telegraph and The Times, between January 1997 and March 2017. Theoretically, it combines Chantal Mouffe’s political theory with Anabela Carvalho’s media theory, in particular her diachronic model of circuits of culture. On this basis, the paper distinguishes five critical discourse periods and shows how a number of critical discourse moments, exactly because of their agonising dimension, have been able to spike media attention and shift the terms of the debate. Starting from this observation, the paper argues that media processes should not only be understood in cyclic terms, but can also trigger non-linear iterative dynamics, leading to upward spirals characterised by thresholds and a gradually increasing level of interest overall, in this case raising the profile of London’s poor air.

Alfred Fletcher’s campaign for black smoke abatement, 1864–96: Anticipating the 1956 Clean Air Act

Many smog episodes occurred during the nineteenth century because of the steadily rising consumption of coal for industrial furnaces, commercial ovens and domestic hearths, but the few legislative efforts to abate black smoke proved ineffectual. It was only in 1956, and following the major London smog episode of 1952, that the government was finally forced into action and parliament approved the Clean Air Act. But two important elements of the 1956 legislation were key parts of an earlier campaign conducted by Alfred Fletcher between 1864 and 1896 and while he was working for the Alkali Inspectorate. The Inspectorate had expected black smoke to be added to the list of regulated noxious vapours but governments and parliament resisted. Access to Fletcher’s private diaries reveal how he remained a leading advocate for abatement of black smoke, and used his position as Chief Inspector from 1884 to conduct a multi-pronged campaign both inside and outside the Inspectorate that included promoting gas as a fuel and the technical evaluation of cleaner appliances.

Environmental Association of Burning Agricultural Biomass in the Indus River Basin.

Intensification of smog episodes, following harvesting of paddy crops in agricultural plains of the Indus basin in the Indian subcontinent, are often attributed to farming practice of burning standing stubble during late autumn (October, November) months. Biomass burning (paddy stubble residual) is a preferred technique to clear farmlands for centuries by farmers in that basin. However, despite stable agricultural landholding and yield, smog is being increasingly associated with burning agricultural biomass, thus creating a paradox. Here, we show that the concentration of smog (NOx, PM2.5, SO2) in the ambient air exceeds the safe threshold limits throughout the entire year in the region. This study argues that agricultural biomass burning is an ephemeral event in the basin that may act as a catalyst to a deteriorated air quality in the entire region. Results further demonstrate that simultaneous saturation of air pollutants along with high ambient moisture content and low wind speeds following the monsoon season are strongly related to aggravated smog events. Findings from this study should help make holistic mitigation and intervention policies to monitor air quality for sustainability of public health in agricultural regions where farming activities are a dominant economic driver for society.