فهرست مطالب namrata jariwala

Most pollutants found in rivers come from the discharge of raw sewage from both point and nonpoint sources. So, monitoring the pollution levels in surface water sources is essential. River pollution monitoring is a real challenge. Using remote sensing, precise outcomes can be achieved with the help of the selection of the right combination of satellite images and algorithms. Generally, established available algorithms are site-specific, indicating that they may not work at all areas on Earth's surface due to differences in altitude, cloud cover, and sun glint. The present work determined Chlorophyll-a concentrations in the Tapi River at various locations using Landsat-8 satellite images and Acolite software from 2017 to 2021 Period. The outcomes reveal that applying the dark spectrum fitting with sun glint correction when processing Landsat-8 satellite images is needed. In the present study, water quality results were obtained very precisely for the months of January, February, November, and December after processing and analysing satellite images. Due to factors such as sun glare, cloud cover, cloud shadow, and haze, the desired effect could not be achieved in the remaining months of the study period. This research provides a solid foundation for estimating the impact of eutrophication in the water body by estimating chlorophyll-a concentration from satellite images.

Pollution from atmospheric particulates is a severe environmental problem of universal concern. Fine and ultra-fine particulates harbour the ability to enter the bloodstream and carry with them trace metals like copper, cadmium, iron, lead, and zinc that can cause toxic and carcinogenic effects. This necessitates an increased emphasis on the detailed chemical characterisation of atmospheric particulates. The current study identified six locations in the Vapi industrial area. In these six locations, coarse particulate matter (PM10) samples were collected simultaneously for 20 days to determine the Elemental Carbon (EC), Organic Carbon (OC), Water-soluble ions (WSIs), and major and trace elements. The concentration of PM10 was observed to be in the range of 115.88 to 226.5 μg/m3, exceeding the NAAQS standard value of 100 ug/m3. The chemical analysis results suggested contributions from total carbon, water-soluble ions, and elements varied between 45 to 48%, 20 to 23%, and 29 to 33% of PM10 mass, respectively. Chemical mass balance (CMB) and Positive matrix factorisation (PMF) models were employed separately for carrying out source apportionment studies. CMB demonstrated influence from various sources: 35% from fossil fuel combustion that included industries, 22.90% from crustal or soil dust, 19.12% from biomass burning, 16.18% from vehicular emissions, and 6.79 % from secondary particulates. The PMF receptor model showed the influence from various sources as 25.75 % from fossil fuel combustion, 22.13 % from crustal or soil dust, 16.95% from vehicular emissions, 14.53% from biomass burning, 11.49% from industrial emissions, and 9.16% from secondary aerosols. Thus, this study shall help in formulating pollution abetment strategies.

The present study examines human exposure to Particulate Matter (PM10) and analyses potential health concerns in the industrial zones of Ankleshwar and Vapi in Gujarat.

For Ankleshwar and Vapi, 120 samples were collected, and characterisation was carried out to determine the concentration of NO3, SO4, NH3, K-S, Na, EC, OC, Al, Si, Fe, K, Ti, Ni, Br, Ca, Cl, Mn, Pb, Cr, Zn, S, V, and Cu in PM10 mass. The health risk from exposure to different trace elements, including both carcinogenic and non-carcinogenic, is evaluated for three distinct paths of ingestion, inhalation, and skin contact.

The Excess Cancer Risk (ECR) values for Cr and Pb for the ingestion pathway and the carcinogenic risks for Cr, Ni, and Pb for the inhalation pathway are both found to be higher than the minimal permissible threshold (1×10−6) for both children and adults for Ankleshwar and Vapi. However, for Ankleshwar and Vapi, the carcinogenic risks from dermal exposure to Cr and Pb are found to be lower than the permissible limit for both adults and children. It is observed that non-carcinogenic Hazard Index (HI) values for the skin contact and ingestion routes are less than 1 for both children and adults for Ankleshwar and Vapi. While the HI value for the
inhalation pathway is found to be larger than the tolerable limit of 1 for both adults and children.

For the purpose of creating sustainable cities and improving the health of the urban population, this study will provide a fundamental basis and help the governing authorities design mandatory pollution prevention and control methods, restoration plans, and systematic monitoring programmes.

Introduction: Receptor models use the chemical characterisation of particulate matter to determine the source and analyse the source contributions. The main aim of this study is to carry out source apportionment of PM10 for industrial locations of Vapi and Ankleshwar in Gujarat, using the Chemical Mass Balance (CMB) receptor model. Materials and methods: At six distinct locations of Ankleshwar and Vapi, respirable dust samplers were used to collect particulate matter on quartz filter sheets for the current study. Filter papers containing PM10 mass were subsequently examined for Water Soluble Ions (WSIs), major and trace elements, elemental and organic carbon followed by source apportionment study. Results: Using CMB, the contributions obtained for Ankleshwar are 27.85% for crustal or soil dust, 26.31% for fossil fuel combustion, 21.06% for vehicle emissions, 14.20% for secondary aerosols, 9.30% for biomass, and 1.20% for industrial emissions. CMB for Vapi revealed the chief source signatures as fossil fuel combustion including industries contributing 35%, crustal or soil dust contributing 22.90%, biomass burning contributing 19.12%, vehicular emissions contributing 16.18%, and secondary aerosols contributing 6.79%. Conclusion: By applying the CMB model, the primary source is found to be crustal or soil dust followed by burning fossil fuels, vehicular emissions, and secondary aerosols for Ankleshwar and Vapi, respectively. A quantitative assessment of source contributions to particulate matter is required to create emission control measures. The findings of this study will be beneficial for the environmental management of particle concentrations in the study region.

In most developing nations, municipal wastewater treatment is limited to aerobic secondary treatments, expensive and ineffective in removing nutrients from treated effluents before discharge, resulting in eutrophication and imbalance in receiving bodies. As a result, the effectiveness of Chlorella vulgaris for primarily treated wastewater collected from a sewage treatment plant during an 8-hour detention time was investigated in this study. Microalgae have been found to efficiently remove organics and nutrients to levels far below the desired limit in the present research. After algal treatment concentration of COD, phosphate and ammonia reduced to 12.43 mg/L (93.75%), 0.04 mg/L (98.40%) and below detectable limit (100%) respectively. In addition, remarkable reduction was found in solids (TSS, TS and TDS) and EC concentration. The use of microalgae resulted in an increase in DO concentration. As a result, introducing Chlorella vulgaris into a wastewater treatment system can lower nutrient and organics contents without any additional treatment.

In developing countries, wastewater treatment is confined to secondary systems. Hence even after treatment, wastewater effluent has a high level of nutrients which causes eutrophication and has destructive impacts on receiving bodies. Literature reveals that phycoremediation can be the best solution to address the problem faced but is time-consuming, ranging from days to weeks. Hence, the present study aimed to determine an optimum detention time for the microalgal system to treat domestic wastewater. The retention time for treatment in the study was divided into an aeration and settling periods. During the study, aeration time varied from 2 hours to 24 hours, followed by 1-hour settling period for each aeration time. Optimum detention time for microalgal treatment was obtained at 11 hours of detention time (10 hours aeration and 1-hour settling). Parameters analyzed during the study were pH, EC, TS, TSS, TDS, nitrate, phosphate, ammonia, COD and DO. However, the main focus was on nutrients (phosphate and ammonia) and organics (COD) removal while determining the optimum detention time. Maximum removal efficiency obtained for COD, ammonia and phosphate for non-filtered effluent was 75.61%, 90.63% and 83.29%, respectively. However, removal efficiency further increased for filtered effluents to 86.34%, 100% and 91.12% for COD, ammonia and phosphate, respectively. Algal treatment offers an ecologically safe and more affordable system for nutrient removal and eliminates the need for tertiary treatment.

Deteriorated air quality in nation like India contributes to the health burden. The AirQ+ is used to estimate short-term and long-term health impact attributable to surface Ozone (O3) in Surat city. Average hourly ozone concentration data and other criteria pollutants retrieved from January 2018 to December 2019 from two monitoring stations (Limbayat and Varachha).

In this study, the Respiratory Mortality (RM), Cardiovascular Mortality (CM), Total Mortality (TM), Hospital Admissions with Cardiovascular Disease (HACVD), and Hospital Admissions with Respiratory Disease (HARD), as well as Respiratory Mortality-Long-Term (LT-RM) were quantified. Baseline Incidence (BI) data were obtained from literature and Relative Risk (RR) values were referred from World Health Organization (WHO). An annual Sum of Maximum 8 h Ozone means over 35 ppb (SOMO35), 70 µg/m3 , used as a predictor of potential long-term health effects.

More ozone concentration were observed in winter and pre-monsoon than concentration formed in southwest monsoon and post-monsoon seasons. The average of O3 concentration for Limbayat are 71.61 (±0.39) µg/m3 and 29.76 (±1.86) µg/m3 and for Varachha are 61.17 9 (±6.15) µg/m3 , 11.32 (±1.35) µg/m3 during 2018 and 2019, respectively and the obtained cumulative number of cases of death are estimated 136, 45, 172 and 18 persons respectively. Total hospital admission due to cardiovascular and respiratory diseases are found
435, 134, 552 and 58 at Limbayat and Varachha during 2018 and 2019. LTRM is attributed to ozone concentration having the most significant value, 6.8% and 4.62% at Limbayat and Varachha in 2018.

More hospital admissions are found than mortality rates using AirQ+ tool. It can be used to estimate public health in context of mortality and morbidity rates which helps to develop air quality management programs and policy makers to reduce the impact of air pollution on health.

The objective of this study was to investigate the sources of tropospheric ozone (O3) precursors in an urban area using principal component analysis. Chemically reactive conventional pollutants such as carbon monoxide (CO), carbon dioxide (CO2), nitric oxide (NO), and nitrogen dioxide (NO2), as well as some selected meteorological parameters such as global solar radiation (SR), air temperature (AT), relative humidity (RH), wind speed (WS), and wind direction (WD), were incorporated in this analysis. Real-time observation data were obtained from two monitoring stations, Limbayat and Varachha, situated in Surat city, India. The occurrence of a peak O3 level in the summer at 5 p.m. proved the well-known fact of interconnection among the temperature, solar radiation, and increment in O3 concentration. The potencies of CO and NOwere remarkable in either the first or second principal component (PC) observed at both locations with more than 45% concentration, which alluded that the main source of O3 was urban transportation and AT contributed with 50% weightage in the PC ascertained key role of photolysis process in the O3 formation.