State Pollution Control Board Research Articles

Air Quality Index Assessment Of An Educational Organization

Abstract Uncontaminated and fresh air is an elementary prerequisite of every living being. But, in this day and age, atmosphere contamination-is certainly the most pervasive kind of environmental-degradation. In the ambience, there are several pollutants which have widespread causing contamination in the air. Pollutants of chief concern include gaseous pollutants like carbon monoxide, ozone, oxides of sulphur and nitrogen, lead etc. These are primary pollutants. Secondary pollutants are particulate matters of different types ranging from fine to high. Every single one of these pollutants can cause a severe menace to human-health. In the current research, sensor-based equipments have been used to monitor and measure the concentration of various pollutants in the air. In the present research work, the effect of different pollutants like PM2.5, PM10, NO2 C02, TVOC, H-CHO, Temperature, Humidity and Noise on air pollution at five stations in the education institute namely RCOEM campus is studied and presented. It had been observed that air pollutants like PM2.5, PM10, and NO2 primarily affected the air quality of RCOEM campus. So, these pollutants are mainly considered to monitor and evaluate air quality of RCOEM campus. After evaluation, air quality index of RCOEM campus was found to be around 94.5. It is within the range of 51-100. So, its falls in the range of satisfactory as per standard norms of central pollution control board and state pollution control board in terms of quality of the air and thereby its contamination.

Environmental clearance and sustainable development: changing paradigm of environmental constitutionalism in Indian

Environmental Clearance (EC) to operate in any industry or establishment is firmly accepted across the globe. It is issued after complying with four stages of Environmental Impact Assessment (EIA) namely screening, scoping, public hearing and appraisal. It stipulates the precautionary conditions to operate (CTO) an industry without causing environmental hazards deploying all necessary environmental protection measures. The MoEF&CC issues EC in India, directly or through its regional authorities. In recent decisions, the Supreme Court of India has allowed post-EC to some of the MSMEs that were operating without EC on account of ignorance of the law on the part of the owner as well as the State Pollution Control Board (SPCB). The post-EC has been permitted to remove procedural irregularity by applying the ‘proportionality principle’ on account of MSME’s contribution to the national economy and livelihood to a significant size of the populace. This research paper finds post-EC anathema to the sustainable development principles.

Seasonal variations in respiratory morbidity in primary care and its correlation with the quality of air in urban Odisha, India.

Poor air quality, especially in urban regions among low-and middle-income countries such as India poses a significant healthcare challenge. Amongst urban areas, metropolitan cities garner the utmost importance for air quality related policies and studies with limited studies from tier II cities which are thought to be relatively immune to air pollution. Hence, we aimed to identify the most frequent respiratory morbidities and explore its correlation with exposure to ambient PM2.5 particles in Bhubaneswar (a tier II city in coastal India), Odisha. A chart review was carried out through data extracted from the records of urban health centres. Data on PM2.5 concentrations were obtained from Odisha State Pollution Control Board. The morbidities were coded by using the International Classification of Primary Care‑2 system (ICPC-2). Descriptive statistics such as incidence of respiratory illnesses was computed across seasons. The ecological correlation between respiratory morbidity patterns and corresponding concentration of PM2.5 in air was analysed for each season. A positive correlation (r = o.94) between PM2.5 and respiratory morbidities was observed. The incidence of respiratory morbidities was 183.31 per 1000 person year. We identified 21 out of 43 respiratory diseases classified under ICPC-2. Upper Respiratory Tract Infection was the most commonly (116.8 per 1000 person year) incident condition. We observed one-fourth increase in the incidence of respiratory illnesses during winters. Respiratory morbidities are common in urban Bhubaneswar which follows a seasonal pattern and are possibly linked with the seasonal variations in levels of PM2.5 particles. Our study highlights that tier II cities are equally prone to health effects of air pollution. Future programmes and policies should take these cities into consideration too.

Thermal Power Plant Pollution Monitoring and Control using Internet of Things

In our day-to-day life, environmental degradation and global warming are essential factors to consider. These problems were caused due to the contamination of water, air, and soil with the waste generated by industries. The pollutants level generated by the industries to be measured and controlled by central and state pollution control board. The air pollutants are the major cause for the deterioration. The existing pollutant monitoring system are complicated, inaccurate, unreliable, delayed intimation and less efficient. Hence in mettur thermal power plant, the internet of things-based pollutant gas monitoring system has been implemented. This system consists of numerous gas sensors, Arduino UNO controller, ESP8266 Wi-Fi module, ubidots server and alarm. The pollutants measured are carbon monoxide (CO), suspended particulate matters (SPM), sulphur oxides (Sox), nitrogen oxides (Nox) in air. When the pollutants level of industry is crossed the limit, the pollution control board has been intimated the exact location of the industry through internet. The pollution control board can automatically send alert SMS and email to the respective industry. This system is suitable for the recent technology industrial internet of things. The electrostatic precipitator has been installed for pollutant control. It is proved that the proposed system is a very good replacement for the conventional monitoring system done by the human. It is acted as user friendly interface for the operation of the system and is reliable. The system has attained improved pollutant control, increased stability, and consistency of the power plant and finally the safety of the employees working in the power plants and mine areas.

Air Quality Data Acquisition Through Temporal Data Modeling

The paper presents a novel temporal data model for acquiring air quality data. The proposed model uses the free and open-source Postgres database with tuple time stamping, resulting in rapid and effective data extraction. The conventional relational database SQL server for data extraction is used in this paper as the foundation for an air quality temporal database model. The air quality data set collected for MDU Rohtak (source: Central Control Room for Air Quality Management - All India) is utilized to test and determine the outcomes of the proposed model. Valid time is used as the time dimension for capturing data values, together with tuple timestamping. The Postgres "tsrange" datatype is used to record the timestamp. To eliminate data duplication, the dataset is cleaned up and normalized. Using the air quality temporal data model, AQI data at various time points is effectively extracted. The present model data extraction is compared with the conventional relational data model using SQL Server database. The air quality data extraction process takes a remarkably minimal amount of time, with an average of 23% lesser time than SQL server database. It takes only a few milliseconds to extract the AQI data, and the results of the experiment are ready immediately after that. The suggested model is helpful in decision-making and offers decision-makers useful information and the tool they need to enhance air quality. The results are relevant to the study of the AQI components by different pollution management agencies like central and state pollution control boards, national green tribunal and Ministry of the Environment for efficient air pollution control measures.

Management of Bio‐Medical Wastes during COVID‐19 Pandemic: A Case Study of Haryana State

AbstractManagement of biomedical waste (BMW) is important for a healthy and safe environment. In order to guarantee cleanliness and hygiene, BMW must be collected, disposed of, and handled appropriately. The world is being affected by the COVID‐19 pandemic, which has spread to various countries. The COVID‐19 pandemic has increased the volume of BMW production, created challenges for both the central and state pollution control boards (SPCB). During this pandemic, the Ministry of Environment, Forestry, and Climate Change (CPCB) devised and implemented various guidelines and standards for proper BMW management. The number of common biomedical waste treatment facilities (CBMWTFs) has risen, and several states and private institutions have built their own BMW treatment facilities. Despite this, some countries do not have enough infrastructures for biomedical waste management (BMWM). Haryana is responsible for a significant percentage of the COVID‐related BMW generation. Despite the fact that the state possesses 11 CBMWTFs, it is unable to handle this BMW properly. Thus, the finding of this study showed that in some Indian states including Haryana, there are insufficient institutional systems for managing biomedical and hazardous wastes during pandemic situations. The study is based on the examination of data on BMW generation and management. The data have been collected from legitimate sources including scientific literatures and annual reported from government websites. The findings are expected to provide the reader with relevant information on the suitability and efficiency of the whole BMW management framework in India.

Treatment and disposal practices of pharmaceutical effluent containing potential antibiotic residues in two states in India and perceptions of various stakeholders on contribution of pharmaceutical effluent to antimicrobial resistance: a qualitative study

BackgroundAntimicrobial resistance (AMR) is a looming pandemic, demanding prompt actions to avert catastrophic consequences. Effluents from pharmaceutical industries containing antimicrobial residues could serve as one of the entry points of these drugs to the environment. This qualitative study explores the treatment and disposal practices of pharmaceutical effluent (PE) containing potential antibiotic residues (ARs) by interviewing major stakeholders. In addition, we assessed their knowledge and perception on contribution of PE to AMR.MethodsThe study was conducted in the two Indian states, Haryana and Telangana and at the federal level. Data was collected by semi-structured in-depth interviews of 29 participants from 17 stakeholders/organizations viz. Central Pollution Control Board (CPCB), State Pollution Control Boards (SPCBs) of Telangana and Haryana, civic body, pharmaceutical manufacturers, pharmaceutical associations and civil society. Data was analyzed using thematic analysis.ResultsThe effluent treatment and disposal practices varied with the multinational companies (MNCs) having advanced technologies whereas the small and medium-scale pharmaceutical companies (SMPCs) having effluent treatment plants as per the regulations but often under-utilized. The presence of ARs in the PE was considered inconsequential by SPCBs and SMPCs and majority of stakeholders imputed other causes as major contributors to AMR. However, the MNCs were well aware of the contribution of PE to AMR and CPCB also considered ARs as direct source of AMR. The central regulators as well as MNCs expressed concerns regarding the current regulations lacking maximum ARs in the PE.ConclusionSetting up regulatory standards for maximum ARs in PE, their implementation and monitoring is an urgent need to curb environmental contribution of ARs to AMR. The findings of our study will help in systematic approach in policy making, awareness programs and capacity-building in dealing with the ARs in PE to combat AMR.

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

Analysis of Water and Air Quality in and Around HIMSWM Treatment Plant- Hyderabad

In recent days the emission of air pollutants in to the environment has grown very quickly. The air pollution is caused due to the smoke emitted by vehicles, industries and other sources. Hyderabad is the largest and capital city of Telangana. It occupies 650km2 on the Deccan Plateau along the banks of the Musiriver, in the northern part of south India. According to the 2011 Census of India, Hyderabad is the fourth-most populous city in India with a population of 6.9 million residents within the city limits, and has a population of 9.7 million residents in the metropolitan region, making it the sixth-most populous metropolitan area in India. With an output of US$74 billion, Hyderabad has the fifth-largest urban economy in India. In India air pollution is monitored by Central Pollution Control Board(CPCB) along with 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 seven 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 the number to 883 to achieve the goal of 1500 by 2024. For our study because of some practical difficulties and analysis of the air quality. Air Pollutants has been collected from the CPCB. The dataset contains City, Date, Time, PM2.5,PM10, NO2, SO2, CO, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into six zones, air quality monitoring sensors are located at 14 places in and around six zones. For our study we selected one sensor that is Secunderabad, which covers the surrounding areas including Jawahar Nagar treatment plant. To analysis the maximum concentration of air pollutants such as PM2.5,PM10,SO2,NO2,CO,O3over the time period of three months (Jan to Mar 2023).Most physio-chemical properties, including as pH, EC, Alkalinity, Chlorides, Cu, Mn, Pb, and Cr, exceeded their acceptable limits at more than three sample sites, according to this study. The water sources were determined to be unsafe for drinking, agricultural, and irrigation uses due to elevated toxicity levels.It was observed that PM2.5,ranges 218 - 403µg/m3 is very poor causes respiratory illness, PM10 range 112 - 360µg/m3 is moderately polluted causes breathing discomfort ( Asthma, lungs), NO2 range 10 – 12ppm is satisfied causes minor breathing discomfort to sensitive people ,O3 range 20 – 35ppm is good causes minor effect, SO2 is 12 – 22ppm is satisfied causes minor breathing discomfort to sensitive people ,CO 109 – 119ppm is severe effects on healthy people and serious impact for those with existing diseases, so continuous air quality monitoring is necessary to protect environment and human health

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

The rapid growth of urban areas and population as well as associated development over recent decades have been a major factor controlling ambient air quality of the urban environment in Kerala (India). Being located at the southwestern fringe of the Indian peninsula, Kerala is one of the regions that has been significantly influenced by the activities in the Indian Ocean. The present study focuses on the effect of the COVID-19 lockdown (in 2021) on ambient air quality in the selected coastal metropolitan areas of Kerala. Although previous research studies reported improvement in ambient air quality in Kerala during the lockdown period, this study demonstrates the potential of onshore transport of air pollutants in controlling the air quality of coastal urban regions during the lockdown period. Data from the ambient air quality monitoring stations of the Kerala State Pollution Control Board in the urban areas of Thiruvananthapuram (TM), Kollam (KL), Kozhikode (KZ), and Kannur (KN) are used for the analysis. Temporal variation in the concentration of air pollutants during the pre-lockdown (PRLD), lockdown (LD), and post-lockdown (PTLD) periods (i.e., 1 March to 31 July) of 2021 is examined to assess the effect of lockdown measures on the National Air Quality Index (AQI). Results indicate a significant decline in the levels of air pollutants and subsequent improvement in air quality in the coastal urban areas. All the effect of lockdown measures has been evident in the AQI, an increase in the concentration of different pollutants including CO, SO2, and NH3 during the LD period suggests contributions from multiple sources including onshore transport due to marine traffic and transboundary transport.

Analysis of physicochemical characteristics of textile and printing industrial effluents and their influence on vegetation near industrial drain areas in Sanganer, Jaipur (Rajasthan)

A significant threat to the ecosystem and the variety of life forms is posed by the massive amount of garbage that has been produced as a result of rapid industrialisation. Up to 20% of industrial water pollution is contributed by the textile industry, one of the largest industries in the world. Rajasthan occupies a unique position in the textile dyeing and printing industry as a significant economic sector. The textile sector is also regarded as one of the major environmental risks. Many dyes, chemicals, and other substances are used by the textile industry to give fabrics the properties they need. Because textile industries use a lot of water and chemicals, their dyeing units produce a huge amount of effluents that are harmful. The volume of effluents produced by these operations is substantial. They not only have detrimental impacts on the soil and water quality, but they also endanger the health of plants and animals. In this investigation, the physicochemical parameters of industrial effluents from four different sites at the Sanganer textile industrial area were studied to access the quality of effluents and results were compared to textile effluent standards prescribed by the Rajasthan State Pollution Control Board. The samples were alkaline besides having high chemical oxygen demand. Two different vegetation areas near Sanganer industrial drainage were also identified and studied to analyse the effects of effluents on the growth of plants. It has been noted from the study that the textile wastewaters exhibit extreme variations from the CPCB guidelines in terms of pH, TDS, and EC. The outcomes also show that the effluents have a pollutant load that is significantly larger than the raw water utilised.

Modelling the impact of Urban Heat Island mitigation strategies on Urban Air Quality

Increasing urban sprawl has caused many severe problems like surge in pollution, rapid climatical variations, and the intensification of temperature in the urban areas, termed Urban Heat Islands (UHI). Population density has caused theconversion of most land areas into cities, and cities have expanded vastly. UHI phenomenon has caused temperature rise in the cities. Most of the metropolitan regions of India are experiencing consequences of UHI and the severity of pollution formation, which is a crucial research area. Since the rising temperature has a direct linkage with urban air pollution; the mitigation measures for UHI are also linked with urban air pollution mitigation; efficacy of mitigation measures of UHI phenomenon in correlation with urban air quality is being studied extensively, which emphasizesthescientific approach and planning concerns of implementation agency to consider the same into urban design and planning aspects. Ahmedabad is one of the growing metropolitan regions of India. The city has grown economically and physically by expanding its boundaries in a radial pattern. This study has attempted temporal assessment of remote sensing data to derive the UHI and the city's growth, and its changing land uses. Assessment has been performed from2008 to 2018 from Landsat data for temperature profile at surface level and type of usage of land of the study area. The spatial profile of Particulate Matter (PM2.5 and PM10) has been generated based on data from the state pollution control board. Four variables, LST, PM2.5, PM10, and LULC, are taken to establish the relationship between all variables present in different layers withthehelp of regression statistical analysis. A strong positive correlation between PM2.5, PM10, and LST has been discovered, which was eventually used to assess the impact of mitigation strategies of UHI, specifically urban greening and a white roof to particulate matter concentrations.

Analysis of Noise Pollution during Dussehra Festival in Bhubaneswar Smart City in India: A Study Using Machine Intelligence Models

Controlling noise pollution in smart cities is a big challenge nowadays due to rise in urbanization and industrialization. As population mass grows, the celebration of yearly festivals such as Dussehra in Bhubaneswar city is also getting popular. However, since this sound pollution is creating a risk to human health, regular monitoring is strictly needed. In this work, the noise pollution level of Bhubaneswar smart city during Dussehra 2020 is predicted using different supervised machine learning (ML) prediction models. The input parameters considered for this work are area or zones of Bhubaneswar city, time at which sound level recorded, equivalent continuous sound level (Leq in dBA), and noise level (high/low compared to the standard value). The data collected for training phase and testing phase by using different ML models is taken from State Pollution Control Board, Odisha, India, for the years 2015–2020. The supervised ML models taken in this work are Decision Tree (DT), Neural Network (NN), k-Nearest Neighbor (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). The predictions of the models are evaluated using Orange 3.26 data analytics tool. From the results, it was found that DT and RF show a higher classification accuracy, 92.5%, than that of other ML models. Moreover, it is observed that the probability of prediction of noise pollution level for the testing dataset for DT is higher for high noise level and for RF is higher for low noise level than other prediction models.

Social, Ethical & Legal aspect of RWA (Resident Welfare Association) Management.

There has been a sharp rise in the number of highrise buildings in almost all cities during the last 10 years and particularly after the declaration of “Smart Cities” by Government of India. So far as the business and management of infrastructure companies are concerned, it is needless to say that it is an established industry. However, after these high rise buildings become habitable, and are handed over to the housing societies or Resident Welfare Associations (Hereafter referred as RWAs), there are many issues and challenges faced by the apartment owners. The motivation behind the study of social, ethical and legal aspect of RWAs/Housing Societies of India is therefore taken up, and we have carried out some study while managing a society from its day-one management. As on date, RWA management is a complex subject as it covers Security, Hospitality, Soft Services, Support Services, House Keeping, Technical Services such as Lift, Electrical Power and Pest Control Management Services. In fact, these facilities have to be managed with a fixed annual budget, collected through Common Area Maintenance (CAM) charges from all the residents. Many of us have the misconception that managing the society within the stipulated CAM (Common Area Maintenance) charges is the only challenge faced by the RWA office bearers, as collection of service changes is always a challenging issue. However, almost all of us forget to abide by the statutory compliances such as Minimum wages, ESI, EPF and GST rules of the government notified from time to time. In addition RWA should follow the norm of City Development Authority, Fire Safety Department, State Pollution Control Board and other statutory bodies, who issue the compliance certificates for the facilities available in the premises after due inspection, which is a mandatory requirement for community living. Therefore, we have made an attempt to highlight this area, so that more study can be carried out and we can have a better living.

Estimating the Water Quality Class of a Major Irrigation Canal in Odisha, India: A Supervised Machine Learning Approach

Contamination of surface water by rapid industrialization, natural and anthropogenic activities is of great concern over the last few decades. Nowadays, canal water systems are no exception to this form of contamination, which results in water quality degradation. To classify the canal water based on the Bureau of Indian Standards (BIS), it was thought to develop a quick and inexpensive approach as an alternative to the time-consuming analysis approach. With this motivation, the present study explores building a machine learning model for water quality classification of a major canal namely the Talaldanda canal operating in the state of Odisha, India. The water quality class is predicted using supervised machine learning (ML) prediction models for the new canal water input parameters. The water quality parameters such as pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), and total coliform (TC) at six strategic locations of the canal from the year 2013-2020 were collected from Odisha State Pollution Control Board for the training phase. The supervised ML models used in the study are Decision Tree (DT), Neural Network (NN), k-NN (k-Nearest Neighbor), Naïve Bayes (NV), Support Vector Machine (SVM), and Random Forest (RF). The predictions of the models are evaluated using the Orange-3.29.3 data analytics tool. When analyzing the performance parameters by sampling the training data into training and testing using cross-validation, the results show that DT has a higher classification accuracy (CA) of 96.6 percent than other ML models. In addition, the likelihood of DT correctly predicting water quality class for the testing dataset is higher than that of other prediction models.

Spatial Interpolation of the Concentrations of Particulate Matter and Carbon Dioxide of Some Selected Tourist Sites in Srinagar City, Jammu and Kashmir, India

This investigation was carried out to monitor and prepare thematic maps of the monthly spatial variability of the ambient concentrations of particulate matter (PM1, PM2.5, PM4, PM10, and TSP) and carbon dioxide (CO2) of some selected tourist sites (viz. Shalimar Garden, Chesmashahi Botanical Garden, Harwan Garden, Nishat Garden, Naseem Bagh, Lal Chowk, and Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K) Shalimar campus of Srinagar city, Kashmir valley from November 2019 to April 2020 using the Inverse Distance Weighting (IDW) interpolation technique in Quantum Geographical Information System (QGIS). Considering Srinagar city as a growing city in terms of population, construction, vehicles, etc., the Jammu and Kashmir State Pollution Control Board in a report on managing air quality in Srinagar city identified the pollution sources in the city with their estimated source proportion as follows: vehicular emission (65-75 %), dust from bad roads (10-15 %), biomass and garbage burning (10-20 %), construction and demolition emissions (5-8 %), minor industrial activities (7-8 %) and other sources (3 %). Thus, Srinagar city’s air quality is deteriorated by these sources. Therefore, this research attempts to reveal the monthly variation and spatial interpolation of particulate matter and CO2¬. The Aerocet 831-Aerosol Mass Monitor and CDM 901-CO2 Monitor were used fortnightly in each month to monitor the ambient concentration of particulate matter and CO2 in the morning, afternoon, and evening and the sampling was carried out by taking three replications. From the data, the average mean morning, afternoon and evening concentrations of each pollutant at all the monitoring sites were as follows respectively: PM1 (67.07, 55.87, 57.62 µg/m3); PM2.5 (129.49, 95.52, 112.50 µg/m3); PM4 (170.44, 121.09, 161.81 µg/m3); PM10 (315.49, 203.09, 383.97 µg/m3); TSP (376.43, 240.49,496.55 µg/m3); and CO2 (595.33, 557.94, 601.07 ppm) showing that the morning and evening concentrations of pollutants were highest in Srinagar city. Also, the data and IDW maps make it clear that there was a statistically significant (p ≤ 0.05) variation of the monthly mean and the average six months concentrations of the monitored pollutants between most of the monitoring sites. This informs us that the concentration of particulate matter and carbon dioxide varies on monthly basis with distance from one location to another in Srinagar city. The correlation of the monthly average of most locations was non-significantly positive between most parameters but significantly strongly positive between PM1, PM2.5, and PM4 at p ≤ 0.01 with each other. Also, there was a significantly strong positive correlation (p ≤ 0.05) between PM4 and PM10, and likewise PM10 and TSP. Thus, indicating that all the monitored parameters increase or decrease with each other simultaneously. It is therefore concluded that the poor air quality of Srinagar city varies with distance as depicted by the data and IDW maps with respect to the monitoring sites and the monitored pollutants. Thus, giving an idea of the pollutants blanket over the city.

A Study of Climate Change in Rajasthan and Its Impact on Flora and Fauna

Climate change, the greatest global challenge, is already a reality for the farmers of Rajasthan. It is increasing the pressure on already scarce resources and if proper measures are not taken, migration towards the cities will soon reach new heights. According to the Rajasthan State Action Plan on Climate Change (RAPCC) report by the Rajasthan State Pollution Control Board prepared with the help of a multi-disciplinary team of experts from TERI with support from GIZ says, 'enough is already known to start action'. And more than the factual situation the action plan that the report suggests is a writing on the wall for farmers.

Significant Changes in Urban Air Quality during Covid-19 Pandemic Lockdown in Rohtak City, India

The global spread of Covid-19 brought a dramatic decrease in vehicular activities, industrial activities and tourism for a limited period of time during the lockdown phase (Year 2020). The variations in ambient air quality were studied in Maharshi Dayanand University, Rohtak city (Haryana state, India) during the lockdown period and pollutants data were gathered from Haryana State Pollution Control Board (HSPCB). The study was conducted for 186 days in three periods; first pre-lockdown, second during the lockdown and third during post-lockdown phase (62 days each). The results showed a significantly decreased the levels of air pollutants through the lockdown season in comparison to pre and post-lockdown concentrations of air pollutants. Mean PM2.5 concentration was found to be higher than their standard limit for the episode of pre and post-lockdown and the mean value of PM2.5 was 2.3 and 2.8 times higher in pre and post-lockdown compared to during the lockdown period. The average air quality index (AQI) during lockdown has shown a 64.1% decrease as compared to pre-lockdown data and a 69.3% reduction from post-lockdown period. Based on one way Analysis of variance (ANOVA) results show ozone accepts the null hypothesis that lockdown had no effect on the atmospheric pollutant concentration due to the increasing pattern of ozone concentration because of a decrease in the concentration of NOx, which breakdown ozone.

Trend Analysis of Sulphur Dioxide in Bangalore & Effectiveness of Control Measures

Bangalore is a city in the Indian state of Karnataka. Many public sector businesses and R&D organisations, such as Hindustan Aeronautics Limited, Indian Space Research Organisation, Airbus, Boeing, General Electric (GE), Nokia, Toyota, and others, are housed here. Bangalore is known as India’s ‘Silicon Valley’, as it is home to many information technology (IT) enterprises. It is classified as a ‘non-attainment city’, meaning that pollution levels do not match the Central Pollution Control Board’s (CPCB) criteria. A high-level computer language called PYTHON was used to draw the trends of the air quality data collected over a 15-year period (2004-2019). PYTHON was used to clean, group, sort and aggregate the collected data to obtain the exceedance factor and draw the trend graphs. The policies of the Central Pollution Control Board (CPCB) and the Karnataka State Pollution Control Board (KSPCB) were examined to better understand the pattern. Over the years, the CPCB and the KSPCB have established a number of policies and guidelines, and the success or failure of these policies was examined in order to gain a better knowledge of the control measures. This report lays the groundwork for future policy implementation in other cities.