Pollution Control Board Research Articles

Sensor based real time air pollutants monitoring for an urban industrial area


 
 
 
 Introduction: This study used economical sensors and an Unmanned Aerial Vehicle (UAV) to examine real-time air pollution assessment for an urban industrial area.
 
 
 
 
 Materials and methods: Using a DJI Phantom 3 Pro, the concentrations in the research area were measured. In the study, Carbon monoxide (CO) and Nitrogen dioxide (NO2) were measured using metal oxide sensors, and Particulate Matter (PM10) was determined using a dust smoke particle sensor. Pollutants were measured at heights of 0.8 m and 10 m for a period of one month.
 
 
 
 
 Results: With an increase in elevation, a gradual drop in pollutant concentration has been seen. High traffic volumes and fuel combustion are to blame for this increase. The concentration of CO, NO2 and PM10 at 0.8 m has been found to be 22.53%, 42.90% and 45.86% respectively higher when compared at 10 m. The main finding of this study is the use of an UAV integrated with sensors for vertical monitoring of the pollutant concentration.
 
 
 
 
 Conclusion: The CO concentration was found to be less than the standard value but near to it, when the data were compared to the Central Pollution Control Board (CPCB) standards. While it was discovered that the measured PM10 concentration was higher than the CPCB standard value, the observed NO2 concentration was determined to be lower than the standard value. Also, given that they produce satisfactory results, low-cost gas sensors can be employed to conduct concentration measurement studies.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Unraveling the Relationship Between Stubble Burning and Air Quality Degradation in Punjab: A Temporal and Spatial Analysis (2019-2022)

Stubble burning in Punjab, India, poses significant environmental challenges, particularly impacting air quality. This study aims to examine the spatial and temporal patterns of stubble burning events and their potential effect on ambient air quality from 2019 to 2022. High-resolution Sentinel-2 satellite imagery was employed to delineate the spatial extent of stubble burning. Burnt areas were identified using the Normalised Burn Ratio (NBR). Air quality was evaluated based on PM2.5 and PM10 concentrations data obtained from the Punjab Pollution Control Board. The Inverse Distance Weighting (IDW) interpolation technique was used to estimate pollution values in areas lacking direct monitoring. The study revealed significant year-to-year variations in areas affected by stubble burning. The smallest burnt areas were recorded in October 2019 and 2021 (209 sq km), while the largest was in 2020 (755.38 sq km). In every year studied, the burnt area in November consistently exceeded that in October, with the largest area (10315 sq km) observed in 2021. PM2.5 and PM10 concentrations also showed annual fluctuations, with the highest recorded in 2020 and 2021. In particular, in October 2020, higher PM2.5 and PM10 levels were detected in the eastern region of Punjab. November consistently exhibited higher PM2.5 and PM10 concentrations than October for all years analysed, peaking in 2021. The spatial and temporal variations of stubble burning events and their relationship with air quality highlight the need for targeted interventions. Understanding these patterns is crucial for mitigating the adverse effects of stubble burning on air quality in Punjab, India. Future research should focus on evaluating the effectiveness of various mitigation strategies.

Forecasting COVID-19 Outbreak Through Fusion of Internet Search, Social Media, and Air Quality Data: A Retrospective Study in Indian Context

This article proposes a machine learning augmented technique to predict the coronavirus disease (COVID-19) outbreak in India by combining Internet search trends along with social media data retrieved from Twitter. A comprehensive list of suitable search words has been used to select a large collection of Tweets, and the Internet search trends of the same keywords have been fetched. First, a lag correlation analysis is conducted to find the number of days, ahead of the current time, required to make an accurate prediction of COVID-19 cases. Second, both shallow and deep learning methods are engaged to predict the number of COVID-19 cases in a specific geospatial location in India. Thereafter, statewise air pollution data collected from the Central Pollution Control Board, Government of India, are amalgamated to understand the effect of air pollution in spreading of COVID-19 disease. The air pollution monitoring parameters have been combined to understand their effects in the prediction of COVID-19 cases in the Indian context. Experimental results reveal that accurate predictions can be made 85 days ahead of the current time using the proposed method (r > 0.85), thereby establishing its ingenuity in the prediction of COVID-19 spread in advance.

Impact of Urbanization on Rivers, India

Abstract: A Central Pollution Control Board (CPCB) report of 2015 brought out the fact that 61,948 million litres of urban sewage is generated on a daily basis in India. But the cities have an installed sewage treatment capacity of only 38 per cent of this. In reality more than this amount goes untreated into the rivers or water bodies as the treatment capacity of major sewage treatment plants (STPs) in the country is around 66 per cent of the installed capacity as per CPCB findings of 2013. As a result, more than 38,000 million litres of waste water goes into the major rivers, water bodies and even percolates into the ground every day. Accelerated transformation of ‘urban riverfront development’ has been pushed along the riverbanks in the last few decades. The riverfront development has been reduced to just cosmetic ‘river beautification’ and unaccountable money spent to increase its real estate and commercial value. Increasing urbanization in the river basin is followed by a number of serious Physical, Chemical & Environmental impacts on the health of the river basin system. We are in the country where rivers are considered as mother but it is unfortunate to say that Indian rivers are much more polluted in comparison of other rivers of the world. Through this Dissertation it will be tried to understand the major reason citizens are not sensitive towards river and develop understanding how we can rejuvenate our tributary rivers.

A multi-model forecasting approach for solid waste generation by integrating demographic and socioeconomic factors: a case study of Prayagraj, India.

Projecting municipal solid waste generation and identifying socioeconomic factors affecting waste generation is crucial for integrated waste management strategies. The present research work focuses on the projection of municipal solid waste (MSW) generation in Prayagraj, India, based on demographics and socioeconomic factors, using long short-term memory (LSTM), autoregressive integrated moving average (ARIMA), and incremental increase models (IIM). The model was integrated with nine socioeconomic variables to improve accuracy. The influence of socioeconomic variables on MSW generation was evaluated using correlation and fuzzy logic approaches. Waste generation data collected from the Central Pollution Control Board (CPCB) from 1997 to 2015 were used to train the models. The results of the correlation study indicate that population growth, employment, and households have a substantial impact on waste generation rates. Root mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R2) suggest that LSTM is the best model to forecast MSW generation in Prayagraj, India. The R2 value indicates that the LSTM is more accurate (0.92) than ARIMA (0.72) and IIM (0.70). LSTM projection indicates that the city will have a population of 1.6 million by 2031, and waste generation will increase by 70.6% in 2031.

Efficiency assessment of 3.2MLD MBR based sewage treatment plant of IFFCO township Aonla, Bareilly, Uttar Pradesh, India

Increasing urbanization and industrialization is continuously putting a pressure on the ground and fresh water resource in form of quality and quantity. Therefore water recycling through wastewater treatment is the need of the present hour. Therefore in the present study the efficiency of the 3.2MLD sewage treatment plant (STP) based on membrane bioreactor technology (MBR) located in Indian Farmers Fertilizer Cooperative Limited (IFFCO) township Aonla, Bareilly, Uttar Pradesh, India was studied. The plant was recently commissioned on 10th of June 2022. The plant shows highest efficiency for turbidity (98.6%) followed by total suspended solids (TSS) (95.7%), chemical oxygen demand (COD) (89.0%), iron (86.7%), and biochemical oxygen demand (BOD) (85.0%). The efficiency for the rest of the parameters is below 50%. The MBR based STP is working efficiently as the values of parameters in treated water is within the discharge standards of central pollution control board (CPCB) listed in The Environment (Protection) Rule, 1986. One of the major problems of MBR based STP reported in literature is membrane fouling which is also rectified in the current treatment plant by using sodium hypochlorite for membrane cleaning.

FSM by Novel Approach of Vermifiltration Adapted to Rural Area: A Case Study of Nachane Village- Ratnagiri District, Maharashtra

-The primary objective of this case study to analysis population and sanitation coverage data for workout and design calculations for Faecal Sludge Treatment Plant (FSTP) Capacity, appropriate low cost & eco-friendly technology i.e., Tiger Biofilter Technology finalization with appropriate Process Units to treat Faecal Sludge properly and final outlet within limit as per recommended norms by Maharashtra Pollution Control Board (MPCB) for selected cluster villages from Ratnagiri Block, Ratnagiri District. After Construction of FSTP, conclusion includes that before and after installation of FSTP scenario of the villages related to Environment. Key Words: Faecal Sludge Management (FSM), Septage, Treatment, Vermifilter, Earthworm, Tiger Biofilter (TBF), Ratnagiri, FSM Chain

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.

A Study of Physical Parameters of Water Samples from Bhima River, Pune City and Their TDS, Conductivity

It is crucial to be aware of the drinking water resources' quality in rural areas. Hydro-geochemical studies are becoming more important for the use of groundwater because there has been a rise in the demand for water recently due to population growth and intensive agricultural operations. One of the major environmental health risks is the quality of drinking water. The use of clean drinking water is the cornerstone of controlling and preventing diseases that are transmitted by water. In this study, it has been determined whether the water quality of Bhima river is suitable for drinking purposes or not. Eight samples were collected from different Locations; each sample was examined for Physiochemical parameters. The Central Water Commission, World Health Organization, and CPCB (Central Pollution Control Board of India) protocols were followed for doing the water's Physiochemical Analysis. Water quality standards and a number of parameters, including pH, Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Temperature, Specific Conductivity, and Total Dissolved Solid, were examined. The pH ranged from being very basic to barely neutral in all of the samples. Among samples, there had been differences in TDS, temperature, pH, and conductivity. The investigation of the river water revealed that it cannot be used as potable water.

A mini review on the catalytic technology development for the mitigation of various air pollutants

The contamination of the air is a significant threat to the environment, which includes both Animalia and plant kingdoms. According to the Central Pollution Control Board of India, the noteworthy contribution to pollution was due to the incidence of particulate matter, oxides of carbon, nitrogen, sulphur, and ozone. Sadly, a few of the world's most polluted cities are situated in India due to several factors, such as a high vehicle count and industrial emissions. In order to reduce these pollutants, various technologies, such as catalytic technology are employed to monitor and control harmful emissions. Catalysis causes no or few secondary pollutants emissions and a high potential for emission reduction. The properties of catalyst materials depend on factors such as synthesis methods and operating parameters. Various catalyst materials and their properties, mechanisms of different catalytic convertors, different kinds of emissions in vehicles, applications of catalysts, and alternatives for noble metals as catalysts are the factors considered in this review paper.

An investigation of the bio-medical waste produced in India during the COVID-19 pandemic and Maharashtra state (pre-COVID-19 and post-COVID-19) analysis: a GIS-based approach

BackgroundThe COVID-19 pandemic exerted substantial pressure on global healthcare systems and facilities, putting the lives of countless individuals at risk. In addition, the treatment of patients during the pandemic resulted in an unprecedented increase in the volume of medical waste generated, including biomedical waste (BMW) or healthcare waste (HCW), which poses a risk of infectious disease transmission. As the second most populous country in the world, India faced a severe challenge in managing its healthcare waste infrastructure during this time (2020–2021). Proper disposal of BMW was of utmost importance to prevent the spread of infectious agents and to safeguard public health.MethodsThe environmental monitoring and management framework of the country is well planned and governed by the Central Pollution Control Board (CPCB), which carefully handles the BMW across the states and union territory of the country. Through the execution of Android based application named ‘COVID19BMW’, India has laid the foundation of identification, classification, data collection, and management regarding the BMW. Further, the temporal scale of BMW generation tracking was further improved from a monthly to a daily basis by using the COVID19BMW tool. This data was used to map the change taken place across the India. Additionally, by using Geographical Information System the BMW is mapped using Choropleth method.ResultsThe current study conducted a national-level analysis of BMW generated during the COVID-19 pandemic in India. The results revealed that, in the year 2020, the seven states and the National Capital Territory (NCT) of Delhi generated the highest amounts of BMW, with Gujarat, Maharashtra, Kerala, Karnataka, Tamil Nadu, Uttar Pradesh, and West Bengal being the top BMW generating states. Additionally, the change detection equation was used to map the changes. The investigation analysed the daily changes in BMW generation between 2020 and 2021 at the national level. The results indicated a significant decreasing trend of -50.35% in BMW generation per day. In the case of Maharashtra state, the change detection analysis for the pre-COVID-19 and post-COVID-19 pandemic periods showed an increased trend of approximately 32%. However, in 2021, a decreasing trend was observed, with a -2.23% reduction in BMW generation compared to 2020 on the daily basis of BMW generation. These findings suggest that the COVID-19 pandemic has influenced BMW generation of waste, and the results can provide insights for improving waste management policies and practices.DiscussionIn this study, a Geographical Information System (GIS) was employed to create a mapped representation of the BMW data at national scale. Further, the study investigated changes in BMW generation in Maharashtra state during the COVID-19 pandemic. Analysis of changes in BMW generation revealed a pattern of BMW generation during the pandemic. The use of GIS technology to track these changes proved to be a valuable tool in providing a synoptic view of the overall BMW condition across India and identifying areas where infectious waste poses a significant threat. The visualisation of data using the GIS technique provided an easy means of identifying hotspots of BMW generation, which is more effective compared to a tabular format.

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.

Inter-seasonal characterization and correlation of Surface Urban Heat Island (SUHI) and Canopy Urban Heat Island (CUHI) in the urbanized environment of Delhi

The Intergovernmental Panel on Climate Change's (IPCC) 6th assessment report warns India about excessive heat. As India's population, urbanization, built-up regions, and concrete expand, vulnerable people may face higher heat risks. Delhi, India's capital and second-largest city, is noted for its excessive heat and air pollution. Heat in Delhi is spatially distributed and varies in magnitude due to built-up area density, the absence of a natural landscape, and other geographical aspects. To pinpoint the hotspot region, the study attempts to characterize Surface Urban Heat Islands (SUHIs) and Canopy Urban Heat Islands (CUHIs). Temporal and seasonal assessment of LST (Land Surface Temperature) using MODIS (Moderate Resolution Imaging Spectroradiometers) data and Ambient Temperature (AT) data from 39 Central Pollution Control Board (CPCB) monitoring stations with a representative radius of 1–5 km used to explore the inter-seasonal variation of SUHI and CUHI over the city in terms of magnitude and extent for 2019, 2020, and 2021. To describe SUHI and CUHI, a two-dimensional Gaussian fitting-based image analysis method is developed. The results show that the region's greatest SUHI magnitude is 2.36 °C in the monsoon season and 2.09 °C in the spring season. In comparison, the maximum CUHI magnitude is 4.74 °C and 3.15 °C in the Autumn and Winter seasons, respectively. The impact of SUHI can be experienced up to 82 km2 from the critical location in Winter and CUHI has 67.83 km2 in Summer. Inter seasonal comparison matrix between SUHI and CUHI demonstrates a positive but modest association due to considerable seasonal parameter fluctuation. UHI hotspot zone is at the centre of the urban with little seasonal fluctuation. South-West (SW) is a hotspot for SUHI and CUHI during the monsoon and spring seasons due to its dense residential and industrial areas. During winter, SUHI indicates SW as the critical zone and CUHI depicts NW. The finding shows that Delhi has a longer duration of hot weather. This study could help to understand UHI in Delhi at the local scale. The study shows UHI's seasonality and potential drivers. Critical hotspots and seasonal shifts should be used in urban development to improve Delhi's thermal environment.

Knowledge and Compliance for Current Guidelines of Biomedical Waste Management among Dental Health Professionals of National Capital Region during COVID 19 Pandemic

Introduction: The highly contagious nature of the coronavirus and improper management of biomedical waste (BMW) may have severe ill effects on the ecosystem. Hence, a study was conducted to evaluate dental health professional knowledge, attitudes, and practice regarding biomedical waste management (BWM) during COVID-19 in the National Capital Region of India. Materials and Methods: A structured, self-administered questionnaire was developed regarding BMW management during COVID-19 and distributed among dental health professionals in dental teaching institution. The collected data was analyzed using Chi-square test along with the one-way analysis of variance, and P < 0.05 were considered significant. Results: More than half of the participants were unaware of the correct disposal of pharmaceutical waste and did not change their masks according to the guidelines given by the Central Pollution Control Board during pandemic. The mean knowledge score of faculty was found to be significantly higher when compared to undergraduate and postgraduate students. Conclusions: This study revealed that the level of knowledge regarding BMW management was inadequate among all the participants in the survey. Hence, dental health professionals should be educated regarding the hazards associated with improper waste disposal at all levels and trained in proper BMW management.

Modelling ambient PM2.5 exposure at an ultra-high resolution and associated health burden in megacity Delhi: exposure reduction target for 2030

Deriving hyperlocal information about fine particulate matter (PM2.5) is critical for quantifying exposure disparities and managing air quality at neighborhood scales in cities. Delhi is one of the most polluted megacities in the world, where ground-based monitoring was limited before 2017. Here we estimate ambient PM2.5 exposure at 100 m × 100 m spatial resolution for the period 2002–2019 using the random forest model. The model-predicted daily and annual PM2.5 show a ten-fold cross-validation R 2 of 0.91 and 0.95 and root mean square error of 19.3 and 9.7 μg m−3, respectively, against coincident ground measurements from the Central Pollution Control Board ground network. Annual mean PM2.5 exposure varied in the range of 90–160 μg m−3 in Delhi, with shifts in local hotspots and a reduction in spatial heterogeneity over the years. Mortality burden attributable to ambient PM2.5 in Delhi increased by 49.7% from 9188 (95% uncertainty interval, UI: 6241–12 161) in 2002 to 13 752 (10 065–19 899) in 2019, out of which only 16% contribution was due to the rise in PM2.5 exposure. The mortality burden in 2002 and 2019 are found to be higher by 10% and 3.1%, respectively, for exposure assessment at 100 m scale relative to the estimates with 1 km scale. The proportion of diseases in excess mortality attributable to ambient PM2.5 exposure remained similar over the years. Delhi can meet the United Nations Sustainable Development Goal 3.4 target of reducing the non-communicable disease burden attributable to PM2.5 by one-third in 2030 relative to 2015 by reducing ambient PM2.5 exposure below the World Health Organization’s first interim target of 35 μg m−3. Our results demonstrate that machine learning can be a useful tool in exposure modelling and air quality management at a hyperlocal scale in cities.

Air Quality Index prediction using machine learning for Ahmedabad city

Prediction of air pollution index may help in traffic routing and identifying serious pollutants. Modeling of the complex relationships between these variables by sophisticated methods in machine learning is a promising field. The objective of this work is to compare the various machine learning methods such as SARIMA, SVM and LSTM for the prediction of air quality index for Ahmedabad city of Gujarat, India. In this research, different preprocessing methods are used to manage the data before providing to the machine learning models. This study is carried out based on the data provided by the Central Pollution Control Board of India and it focuses on the support vector machine algorithm with RBF kernel model. So, that the results availed are comparatively better as compared to other kernels of the support vector machine models as well as SARIMA and LSTM models for Ahmedabad city.

Monitoring of traffic noise pollution in urban Patna, Bihar, India

Road traffic noise is a major environmental issue that has been faced by many people around the world, including in Patna city. There is a need to investigate noise regularly because of the rapidly increasing traffic volume. This study focuses on the estimation of the urban traffic noise level in Patna, Bihar, India. Based on a preliminary survey of traffic volume, 12 stations were selected. A sound meter was used to measure the “A”-weighted noise level. Noise parameters like L10, L50, and L90 were evaluated from the collected noise data. And these noise parameters were used for the calculation of noise pollution indices like Noise Climate (NC), Equivalent Continuous Noise Level (Leq), Noise Pollution Level (NPL), and Traffic Noise Index (TNI). These noise indices compare with the standard limit of the Central Pollution Control Board (CPCB). The equivalent continuous noise level (Leq) measured at different locations in the commercial cum residential zones varies from 77.7 dBA to 92.5 dBA, which is higher than the prescribed noise level limit of the CPCB. Other noise indices, such as the NPL and TNI, are found to range from 88.2 dBA to 111.5 dBA and 77.9 dBA to 125.9 dBA, respectively, at various sampling locations and are also higher than the prescribed limit of the CPCB. The noise level of all stations was found to exceed the prescribed limit of the CPCB. There is a strong correlation exists between NPL with TNI and NC (r > 0.5). Also, there is a strong correlation exists between Leq with NPL (r > 0.5). Using NPL as a dependent variable and TNI, NC, and Leq as independent variables, the regression model was completely validated.