Pollution In India Research Articles

Modeling of mercury deposition in India: evaluating emission inventories and anthropogenic impacts.

Mercury (Hg), a ubiquitous atmospheric trace metal posing serious health risks, originates from natural and anthropogenic sources. India, the world's second-largest Hg emitter and a signatory to the Minamata Convention, is committed to reducing these emissions. However, critical gaps exist in our understanding of the spatial and temporal distribution of Hg across the vast Indian subcontinent due to limited observational data. This study addresses this gap by employing the GEOS-Chem model with various emission inventories (UNEP2010, WHET, EDGAR, STREETS, and UNEP2015) to simulate Hg variability across the Asian domain, with a specific focus on India from 2013 to 2017. Model performance was evaluated using ground-based GMOS observations and available literature data. Emission inventory performance varied across different observational stations. Hence, we employed ensemble results from all inventories. The maximum relative bias for Total Gaseous Mercury (TGM) and Gaseous Elemental Mercury (GEM; Hg0) concentrations is about ±20%, indicating simulations with sufficient accuracy. Total Hg wet deposition fluxes are highest over the Western Ghats and the Himalayan foothills due to higher rainfall. During the monsoon, the Hg wet deposition flux is about 65.4% of the annual wet deposition flux. Moreover, westerly winds cause higher wet deposition in summer over Northern and Eastern India. Total Hg dry deposition flux accounts for 72-74% of total deposition over India. Hg0 dry deposition fluxes are higher over Eastern India, which correlates strongly with the leaf area index. Excluding Indian anthropogenic emissions from the model simulations resulted in a substantial decrease (21.9% and 33.5%) in wet and total Hg deposition fluxes, highlighting the dominant role of human activities in Hg pollution in India.

Survey on Air Pollution in India: Insights, Predictions, and Mixed Gas Analysis for an Improved Future

Abstract: The sustenance of life on Earth is intricately tied to the quality of our air. But this precious resource is facing mounting threats from the harmful consequences of rapid industrialization, transportation networks, and the everyday practices of modern living. This research paper addresses the pressing issue of rising air pollution in India, with a strong emphasis on environmental well-being. Utilizing data from the Central Pollution Control Board of India (CPCB), our project focuses on air quality visualization, pollution prediction, and mixed gas analysis. We have developed an intuitive web-based platform for visualizing air quality data, offering a valuable tool for the public and policymakers. Additionally, predictive models are employed to anticipate pollution levels, facilitating timely intervention and mitigation. Our mixed gas analysis sheds light on the composition of atmospheric gases, enhancing our understanding of pollution sources. Through this interdisciplinary approach, our research aims to provide a comprehensive solution to combat air pollution, fostering environmental sustainability and public health improvement in India

How will climate change affect ambient air pollution and what can policy-makers do now? Lessons from India

Abstract Air pollution is a growing concern in India, and its adverse health effects are well documented. Climate change is likely to exacerbate this problem by altering weather patterns and increasing the frequency and severity of extreme events. This paper examines the potential impact of climate change on ambient air pollution in India and its implications for policy design. Our analysis reveals that pollution in India is highly sensitive to variation in weather, particularly in the densely populated Indus-Gangetic Plain. Using our estimated relationship between weather and pollution, we predict that changing weather patterns will increase average PM2.5 concentrations by 3.1 µg/m3, leading to a loss of 364 million years of life expectancy. To address this challenge, we propose an emissions fee calibrated to be highest in regions most vulnerable to persistently high levels of pollution and most sensitive to future deterioration in air quality due to climate change.

Status of River Water in India: A Review

Water means life which is a cyclic, inexhaustible, renewable prime natural resource. It is both a basic human need and a valued asset possessed by all living creatures. Hydrosphere, cryosphere, atmosphere, lithosphere and biosphere are all sources of water around the world. Water covers 71 % of the earth’s surface but only 1% of water is potable. Rivers like the Ganga, Yamuna and Sabarmati are revered throughout India. River water is critical for commercial and industrial development, hydroelectric power generation, agriculture, new multipurpose dams and tourism attractions. However, the presence of different contaminants such as pesticides, heavy metals, organic waste, chemical waste and direct sewage discharge has harmed the river's water quality. In India, river water pollution is a major issue that has harmed not only human and animal health, but also the economy of the country. In this review, a substantial number of studies on river water pollution in India are examined. Data on various physical, chemical, and biological characteristics are carefully analysed and interpreted, and it is discovered that river water in India is severely polluted. Furthermore, after careful interpretation of data and discussions published in research articles, this review explains the interrelationships among distinct physical and chemical parameters. EC and TDS are complementary to each other and temperature has a direct impact on pH and DO levels. Also, BOD and DO are reciprocally related with each other. This review provides a concise set of guidelines for assessing river water pollution and calculation of water quality index.

A conversation on air pollution in India

A conversation on air pollution in India

An environmental justice analysis of air pollution in India

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 μg/m3 (95% CI 0.062 μg/m3, 0.192 μg/m3) and 0.199 μg/m3 (95% CI 0.116 μg/m3, 0.283 μg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.

Court Decisions and Air Pollution: Evidence from Ten Million Penal Cases in India

This study explores the relationship between air pollution and judicial rulings. Although environmental factors should not affect judicial decisions, realists contend that there is substantial room for external factors to transpire into sentencing and sway human reasoning. We hypothesize that air pollution is one of these factors. Using Poisson panel models and instrumental variable techniques, we show that exposure leads to more convictions. We posit that this effect occurs because the impact of exposure on the central nervous system changes the cognitive performance and empathy of judges. Back-of-the-envelope calculations suggest that decreasing average air pollution in India by one standard deviation would lead to up to 145,000 fewer convictions regarding currently active cases.

Identification of Sources Causing Air Pollution in Indian Cities Using Hierarchical Agglomerative Cluster Analysis

The distressing levels of air pollution in India is becoming health hazard to the inhabitants. It's important to note that due to the nation's continued urbanisation and its heavy reliance on coal for electricity generation, air pollution is expected to get worse in some areas of India over the next few decades. Present study aims to address the issue by identifying the sources causing air pollution using Hierarchical Agglomerative Cluster Analysis [HACA]. Two years daily data (2018 and 2019), downloaded from publicly available source Kaggle.com of sixteen selected air pollution monitoring stations was used for the study. The stations were selected based upon diversified environmental conditions and local sources. HACA was successful in grouping the monitoring stations into four clusters based on their average Air Quality Index (AQI) level. These four clusters are named as Low Pollution, Moderate Pollution, High Pollution and Very High Pollution Region [LPR, MPR, HPR and VHPR] with average AQI 96; 135; 173 and 227 respectively. Discriminant Analysis (DA) confirmed the resulting clusters with 100% accuracy. It was found that stations with similar environmental factors, regional sources, and pollution amounts were clustered together. Despite numerous actions taken by the authorities to reduce air pollution, it was noticed that topographical conditions play an essential role in the rise of pollution. This study helps to implement different strategies by the authorities’ concern based on local sources and topographical conditions.

Assessing the costs of ozone pollution in India for wheat producers, consumers, and government food welfare policies

We assess wheat yield losses occurring due to ozone pollution in India and its economic burden on producers, consumers, and the government. Applying an ozone flux-based risk assessment, we show that ambient ozone levels caused a mean 14.18% reduction in wheat yields during 2008 to 2012. Furthermore, irrigated wheat was particularly sensitive to ozone-induced yield losses, indicating that ozone pollution could undermine climate-change adaptation efforts through irrigation expansion. Applying an economic model, we examine the effects of a counterfactual, "pollution-free" scenario on yield losses, wheat prices, consumer and producer welfare, and government costs. We explore three policy scenarios in which the government support farmers at observed levels of either procurement prices (fixed-price), procurement quantities (fixed-procurement), or procurement expenditure (fixed-expenditure). In pollution-free conditions, the fixed-price scenario absorbs the fall in prices, thus increasing producer welfare by USD 2.7 billion, but total welfare decreases by USD 0.24 billion as government costs increase (USD 2.9 billion). In the fixed-procurement and fixed-expenditure scenarios, ozone mitigation allows wheat prices to fall by 38.19 to 42.96%. The producers lose by USD 5.10 to 6.01 billion, but the gains to consumers and governments (USD 8.7 to 10.2 billion) outweigh these losses. These findings show that the government and consumers primarily bear the costs of ozone pollution. For pollution mitigation to optimally benefit wheat production and maximize social welfare, new approaches to support producers other than fixed-price grain procurement may be required. We also emphasize the need to consider air pollution in programs to improve agricultural resilience to climate change.

Discordant future climate-driven changes in winter PM2.5 pollution across India under a warming climate

India’s megacities have been suffering from frequent winter particulate matter (PM2.5) pollution episodes, and how impacts of meteorology on air quality will evolve with time under a warming climate remains a concern. In this study, we identified conducive meteorological weather conditions in 5 megacities across India and found that quantile regression models can better describe the meteorological impacts under high pollution level and capture more observed high PM2.5 events than linear regression. The future climate-driven changes in winter PM2.5 pollution in India were offered with quantile regression models using Coupled Model Intercomparison Project 6 simulations under the SSP585 and SSP245 scenarios. Under SSP585 scenario, northern Indian megacities are likely to suffer from a stagnant weather condition in the near future, and higher boundary layer height and more atmospheric dispersion conditions during the second half of 21st century. Compared with the mean levels over 1990–2019, New Delhi and Kolkata would experience 6.1 and 5.7 more PM2.5 exceedances per season over 2030–2059 and 4.1 and 2.5 fewer exceedances per season during 2070–2099, respectively. Owing to increasing surface humidity and boundary layer height, air quality is projected to improve in Mumbai and Hyderabad with more than 6.1 and 1.2 fewer exceedances per season over 2050–2099. However, more than 6 exceedances will occur in Chennai due to enhanced lower-tropospheric stability. The negative impact of future meteorology on PM2.5 exceedances would become weak under SSP245. Our results can provide references for the Indian government to optimize their emission control plans to minimize adverse impacts of air quality on health, ecosystem, and climate.

Impact of Growth of Automobile Industry on Air Pollution in India

Impact of Growth of Automobile Industry on Air Pollution in India

The biomethane generation potential of wastes and wastewaters from the sericulture, fisheries, and agro-industrial sectors in India

The global problem of climate change and difficulties related to energy access, air pollution, and water pollution in India (and other developing countries) necessitate the need to look for alternative clean, local, and low-carbon energy sources for displacing fossil fuels. This paper analyses the existing waste-to-biomethane potential of the sericulture, fisheries, and agro-industrial sectors in India, which have mostly been neglected previously in India's waste-to-energy assessments. In the process, the volume of wastewaters generated from India's agro-industries are also estimated, data on which are scarce in the academic and grey literature. India's annual biomethane potentials (for 2018–19) were found to be 88 million m3, 207 million m3, and 3514 million m3 from the three sectors, respectively. To assess the energy generation potential of these wastes, a comprehensive literature review was conducted and data on residue-to-product ratios, results of proximate analyses, and specific biomethane yields were either compiled or calculated. The gross thermal energy and electricity generation potentials are 132 PJ and 22 TWh respectively; between 19 and 95 million people can benefit from such waste-to-energy production, depending on the end use of the biomethane for electricity generation or as a domestic cooking fuel. The corresponding mean removal of organic pollution from India's surface waters can be between 5.5 and 9.3 million tonnes of chemical oxygen demand. For the end uses of biomethane in electricity generation and cooking, the cumulative GHG abatements attributable to these industries are 11.4 million tonnes and 6.8 million tonnes of carbon dioxide-equivalent respectively. Besides the numeric results reported, this paper also contains a comprehensive compilation of data on waste characteristics and biomethane productions. The present status of biogas generation and related policy implementation in India are reviewed and discussed. Overall, our findings indicate that biomethane from these wastes and wastewaters can generate substantial energy for India, is in line with recent government policies, and can aid India in achieving six Sustainable Development Goals of the United Nations.

Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?

The onset of the novel Coronavirus (COVID-19) has impacted all sectors of society. To avoid the rapid spread of this virus, the Government of India imposed a nationwide lockdown in four phases. Lockdown, due to COVID-19 pandemic, resulted a decline in pollution in India in general and in dense cities in particular. Data on key air quality indicators were collected, imputed, and compiled for the period 1st August 2018 to 31st May 2020 for India's four megacities, namely Delhi, Mumbai, Kolkata, and Hyderabad. Autoregressive integrated moving average (ARIMA) model and machine learning technique e.g. Artificial Neural Network (ANN) with the inclusion of lockdown dummy in both the models have been applied to examine the impact of anthropogenic activity on air quality parameters. The number of indicators having significant lockdown dummy are six (PM2.5, PM10, NOx, CO, benzene, and AQI), five (PM2.5, PM10, NOx, SO2 and benzene), five (PM10, NOx, CO, benzene and AQI) and three (PM2.5, PM10, and AQI) for Delhi, Kolkata, Mumbai and Hyderabad respectively. It was also observed that the prediction accuracy significantly improved when a lockdown dummy was incorporated. The highest reduction in Mean Absolute Percentage Error (MAPE) is found for CO in Hyderabad (28.98%) followed by the NOx in Delhi (28.55%). Overall, it can be concluded that there is a significant decline in the value of air quality parameters in the lockdown period as compared to the same time phase in the previous year. Insights from the COVID-19 pandemic will help to achieve significant improvement in ambient air quality while keeping economic growth in mind.

Assessment of Air Quality Around Heavy Traffic Zone in Hyderabad

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city. Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB .The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 5 stations based heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 3 PM2.5,range 183.7 – 163.26µg/m3 is very poor as per AQI, PM10 range 151.11 to 137.76 µg/m3 is moderately polluted as per AQI, NO2 range 61.03 to 76.4 µg/m3 is satisfied as per AQI, NO3 range 1.196 to 7.96 is good, SO2 is 6.34 to 55.83 µg/m3 is satisfied ,CO 70.35 to 71.97 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health

Air Quality Monitoring at Heavy Traffic Zone in Hyderabad

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city.Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB.The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 2 stations Newmalakpet and Sanathnagar based on heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 52PM2.5,range 120 to 249 µg/m3 is very poor as per AQI, PM10 range 101.2 to 171 µg/m3 is moderately polluted as per AQI, NO2 range 7.46 to 32.9 µg/m3 is good as per AQI, NH3 range3.06 to 5.44 is good as per AQI, SO2 is 12.97 to 8.53 µg/m3 is good ,CO 61.83 to 71.9 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health

Environmental Pollution In India: Connecting Particular Pollution To Social Disadvantage

Environmental Pollution In India: Connecting Particular Pollution To Social Disadvantage

Lotka-Volterra analysis of river Ganga pollution in India

Water quality indices (WQI) are a useful tool to assess river water pollution. We defined pollution shares (indicating the relative importance of pollutants) from a WQI and studied their dynamics. Using open data from 2012 to 2020 for 105 monitoring stations along river Ganga in India, we fitted systems of generalized Lotka-Volterra (LV) differential equations to these shares. We used autonomous LV-systems (the interaction coefficients were constant) and LV-systems with variable (linear) interaction coefficients. 28 of the 105 stations had sufficient data for these models, whereby for 10 stations the autonomous system fitted well to all timeseries of the eight considered pollutants, and for 9 stations the model with linear interaction coefficients. For them we defined three candidates for “importance-growth indicators”: (a) the interaction coefficients of the autonomous LV-system, (b) the leading coefficient of the interaction coefficient of the system with linear coefficients, and (c) the roots of these linear coefficients. We explored the variability of the indicators and applied them to identify stations with a similar temporal evolution of the pollutants. Further, we suggested applications to wastewater management, as at several stations the indicators (a) and (b) forecasted an increasing relative importance of nitrates/nitrites, which currently pose no problems but finally may require an upgrading of existing wastewater treatment.

Forecasting Particulate Matter Emissions Using Time Series Models

Environmental pollution is a serious concern nowadays with its disastrous impact on living organisms. In several types of pollution, Air pollution takes on a crucial role by directly affecting the respiratory system and causing fatal diseases in humans. Air pollution is a mixture of gaseous and particulate matter interweaved by different sources and emanating into the atmosphere. In particular, particle pollutants are critical in growing air pollution in India’s main cities. Forecasting the particulate matter could mitigate the complications caused by it. The employment of a model to predict future values based on previously observed values is known as time series forecasting. In this paper, the PM2.5 pollutant emission data recorded at the Kodungaiyur region of Chennai city were forecasted using three-time series models. The standard ARIMA model is compared with the deep learning-based LSTM model and Facebook’s developed Prophet algorithm. This comparison helps to identify an appropriate forecasting model for PM2.5 pollutant emission. The Root Mean Squared Error (RMSE) acquired from experimental findings is used to compare model performances.

Evaluation of Water Quality, Urinary Mercury & Arsenic Investigation and Survey of Diseases Associated with Drinking Water Sources

Background: Contamination of freshwater sources can be caused by both anthropogenic and natural processes. WHO reported that 1.1 billion people worldwide consume contaminated water, and the majority of diarrheal diseases (88%) are caused by it. According to Central Pollution Control Board (CPCB), Maharashtra along with two other states contribute 80% of hazardous waste, including heavy metal pollution in India. Certain dissolved heavy metals are easily absorbed by aquatic organisms and can enter the body through drinking water, skin absorption, and biological chains, posing a health risk. Hence, the main objectives of the study were, (1) surface and groundwater quality assessment, (2) to determine the association of diseases/symptoms with different sources of drinking water used, and (3) urinary Mercury (Hg) and Arsenic (As) levels investigation in the study population. Methods: The cross-sectional study was conducted in the industrialized city of Solapur, Maharashtra, India. The study area was limited to 25 km around the industrial hub. A total of 557 people were randomly selected for the survey, with consumers from all four types of water sources i.e., surface, handpump, wells, and municipal water. Spot urine samples were collected for estimating Hg and As levels after considering inclusion and exclusion criteria. People under treatment for tuberculosis, cancer, and chronic heart, lung, or kidney ailments were excluded. Also, pregnant and lactating women were not included. One surface water, one municipal water, and five handpump water samples were collected for evaluating water quality. Samples were analyzed for pH, Total Dissolved Solids (TDS), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Fluoride, Ammonia, Mercury, Arsenic, Cadmium, Lead, Nickel, Copper, Zinc, Chromium, and Manganese. Results: The water samples were observed to be neutral to slightly basic. TDS was between 410 to 1898 mg/L for groundwater whereas for surface water was 378 & 450 mg/L. F concentration ranged between 0.4 to 0.9 mg/L, Zn from 0.32 to 0.57 mg/L, and NH3 was found to be <0.1 mg/L. Out of 557 people, 43 (7.7%) used surface water, 194 (34.8%) used handpump, 64 (11.5%) used well, and 256 (46%) used municipal water. Among surface water users, 14 (32.5%) people reported frequent loose stools (p-value <0.05), and 11 (25.5%) people reported frequent abdominal pain. Handpump and well water users majorly reported frequent abdominal pain and gastric discomfort (p-value <0.05) respectively. 47 people were selected for estimating urinary Hg and As levels after considering exclusion and inclusion criteria. The mean value of urinary Hg & As are 4.91 ± 0.280 & 42.04 ± 2.635 µg/L respectively. Conclusion: In our study frequent loose stools, abdominal pain, and gastric discomfort were associated with the various sources of drinking water. Urine Hg levels were above the permissible reference value set by NHANES (USA) Survey. Additionally, it is advised that frequent drinking water monitoring be implemented in the vicinity of the industrial hub since metal accumulation may be dangerous to consumers when it is present in excess, and if found higher, necessary action should be taken to reduce exposure.

Monitoring Vehicle Noise and Pollution with a Smart IOT System

The ozone layer is deteriorating due to pollution in India and other parts of the world, which also greatly increases air pollution. The project's objective is to create a carbon monoxide detection system that can measure values in any application as well as track and monitor CO levels. The Internet of Things (IoT) connects things (physical objects like lights, phones, and cars) to the Internet by acting as a bridge connector to the current Internet infrastructure.Currently, only after receiving a fitness certificate (FC) from the RTO office can the vehicle's emissions be verified utilizing pollution control stations positioned across various cities. Health certificates for private vehicles are valid for 15 years, after which they must be renewed every 5 years. The fitness certificate is provided for two years and then renewed annually when it comes to vehicles. Until FC is fitted, this method prohibits us from identifying emissions brought on by car maintenance.Blynk is a cloud-based IoT analytics platform that allows you to gather, view, and analyze live data streams. This article focuses on using an MQ7 Gas sensor to prevent accidents caused by vehicle-generated carbon monoxide.