Life cycle assessment of municipal solid waste generated from hilly cities in India - A case study.

Improper disposal of waste poses a grave environmental threat, contributing to pollution of air, water, and soil. It is necessary to address this issue in order to mitigate the adverse effects of solid waste on both the environment and public health. In many developing nations, municipal authorities of bigger cities are enduring significant challenges in proper management of waste. The present study evaluates the impacts of various waste management alternative scenarios for environmental impacts for the selected study locations using Life Cycle Assessment (LCA) methodology. The methodology comprised of five different scenarios of waste management including an existing baseline scenario. In this context, the environmental impact categories analyzed were Global Warming potential (GWP), Acidification potential (AP), Eutrophication potential (EP) and Human Toxicity potential (HTP). The results indicated that amongst all the proposed scenarios, Scenario 1 and 4 exhibited the maximum and minimum environmental impacts respectively. The study revealed that least greenhouse gas emissions, acidification potential, eutrophication potential and human toxicity potential were comparatively lesser for scenario 4 varying from 5.65 to 11.36 kg CO2eq t-1; 1.24-3.345 kg SO2eq t-1, EP 0.19-0.68 kg PO4eq t-1, and 0.35-4.22 kg 1,4-DBeq t-1 respectively. Further, a sensitivity analysis was also performed to evaluate the influence of recycling rate of valuable resources in all the considered scenarios. The sensitivity analysis indicated an inversely proportional relation between change in recycling rate and total environmental burdens.

Assessment of gas generation and energy recovery from municipal solid waste in Kanpur city, India.

The study reported herein presents the methane generation potential from municipal solid waste (MSW) generated in Kanpur city using four established methods, namely: the IPCC Default Method (DM), EPER Germany, The IPCC First Order Decay (FOD) method, and the Modified Triangular Method (MTM). Results revealed that the average maximum and minimum emissions with respect to total MSW generated and considered over the study period were obtained in the IPCC Default Method (19.17Gg/year) and the MTM (1.00Gg/year), respectively. Furthermore, the sensitivity analysis carried out revealed that the MTM method is the least uncertain method in predicting the methane emissions. Energy generation using the Yedla method and the Stoichiometric method was also carried out, highlighting the potential for energy recovery using methane emissions. The total energy generation potential using the Yedla method over the entire study period was determined to be 924 TJ, with an increased potential of 30% between the periods of 2022 to 2031. According to the study, there exists significant potential for effectively managing the greenhouse gas emissions from open dumpsite by harnessing the methane produced and using it for energy generation.

Assessment of landfill gases by LandGEM and energy recovery potential from municipal solid waste of Kanpur city, India.

The world due to increased urbanization and globalization is facing major environmental challenges. Anthropogenic emissions of Greenhouse gases (GHG) like carbon dioxide and methane are on the rise and unsustainable which needs to be regulated. Open dumping of Municipal Solid Waste (MSW) contributes to generation of greenhouse gases like carbon dioxide and methane. This is because large fractions of the waste open dumped are organic in nature which undergoes anaerobic decomposition leading to generation of GHGs. In particular, methane has a high potential for energy generation and if utilized could be highly beneficial. The present study assesses the generation of landfill gases, primarily methane generation potential from MSW generated in Kanpur city using LandGEM 3.02 version model developed by USEPA for the period 2015-2030. It was observed from the study that the cumulative LFGs generation, methane emission and energy recovery potential estimated as 233.44 × 106 m3, 116 × 106 m3 and 858.14 × 106 MJ respectively. Uncertainty analysis carried out showed that variation in methane emissions maybe attributed to input parameters of k and Lo of the LandGEM model. The study shows that there exists high potential to control the greenhouse gas emissions by utilizing the methane generated for energy production.

Efficiency evaluation of sewage treatment plants in Delhi, India, using tolerance-based data envelope analysis.

Correct and effective performance evaluation of wastewater treatment plants is a tough task because of the complex biological, physico-chemical, and biochemical processes and associated variables affecting their performance. Conventionally, the efficiency of sewage treatment plants (STPs) are obtained using some index relating pollutant removal efficiency with energy used or costs. These indicators consider only one variable at a time. This leads to incorrect assessment of efficiency, which in turn could adversely affect decision-making of the regulatory authorities. The data envelope analysis (DEA) method utilizes a Linear programming technique which can handle multiple input/output variables without requiring the cost function. This makes it an appropriate tool for assessing the relative efficiency of treatment plants. The present study assess the efficiency of 30 STPs in Delhi, India, using the tolerance-based DEA model utilizing the variable return of scale (VRS). The uncertainty was incorporated into the model using the tolerance measure. The model is solved using the "Add on" option in spreadsheet toolbox of excel solver. Results reveal that out of the 30 plants considered for the study, 6 are performing well (20%). Further, it was observed that a slight change in the input data leads to instability of the efficiency results. Lastly, the ranking is used to determine the treatment plant with best efficiency under all scenarios for the larger period of the year. Such studies will help in chalking out the best management practices that could be adopted by other regulatory authorities.

Water quality assessment in two lakes of Panchkula, Haryana, using GIS: case study on seasonal and depth wise variations.

Water is the most important commodity available on earth and exists as both surface and sub-surface sources, but increased water pollution has reduced its potability. In this context, it has become imperative to regularly monitor the water quality. In situ and laboratory experimental procedures involve point wise collection of samples for quality determination which are too elaborative and time consuming. As such, the use of methods like Geographic Information System (GIS) modelling if used in collaboration with the traditional methods can prove to be a great tool as they are less expensive and gives a complete spatial resolution of the study site. Therefore, the present study focuses on the determination of water quality using traditional methods in collaboration with GIS modelling system using the inverse distance weighing (IDW) method for two natural lakes in Haryana. The IDW technique was used to interpolate parameters like temperature, dissolved oxygen (DO), nitrates (NO3) and total phosphorous (TP) as they represent the effects of recent and old pollution in lake waters at different depths. These parameters were interpolated for determining the overall water quality status for the lakes. The collaboration can prove to be of great practical significance in today's time by giving an elaborative view of the present water quality status, easing daily telemetric monitoring of the sites as well as give an opportunity for futuristic modelling. The technique can work for almost all the sites around the globe which have either not been evaluated from quality aspect or are inaccessible for monitoring. Parameters like temperature and DO show significant depth wise and seasonal variations for both the lakes with highest values observed at the surface levels, whereas the NO3 and TP represented effects of point pollution sources to a smaller extent. The maximum value of temperature was determined to be of 30.7 °C and 9 mg/l for DO and was recorded at the surface of lakes 2 and 1, respectively. Further, nitrate and phosphorous concentrations were observed to have maximum values of 0.99 and 0.5 mg/l at the centre of the lake 1 for monsoon season due to influx of pollutants and settlements in the bed. The primary reason for the variation of water quality may be attributed to increased sedimentation at the bottom of lake due to agricultural activities in the vicinity which creates impacts on different hydrodynamic processes leading to increased levels of TP and NO3 concentrations. Further, increased recreational activities lead to induced variations in the water quality as well.

Exposure assessment of PM2.5 in temple premises and crematoriums in Kanpur, India.

Regular use of incense and earthen lamps in temples leads to the release of particulate matter (PM), airborne flecks, and gaseous pollutants. Similarly, the cremation of dead bodies using timber and other accessories such as incense, organic chemicals containing carbon, and clothes generates air pollutants. It is currently unclear how much emissions and exposure these activities may lead. This work attempts to fill this gap in our understanding by assessing the associated emissions of PM2.5 and the corresponding exposure. Ten temples and two cremation grounds were considered for the sampling of PM2.5. The average PM2.5 concentration at the ten temples and the two crematoriums was found to be 658.30 ± 112.63 µg/m3 and 1043.50 ± 191.63 µg/m3, respectively. The range of real-time PM2.5 data obtained from the nearest twelve stations located in the vicinity was 113-191 µg/m3. The exposure assessment in terms of deposition dose was carried out using the ICRP model. The maximum and minimum total respiratory deposition dose rate for PM2.5 for temples was 175.75 µg/min and 101.15 µg/min, respectively. For crematoriums, the maximum and minimum value of same was 252.3 µg/min and 194.31 µg/min, respectively, for an exposure period of 10 min.

Short-term impacts of air pollutants in three megacities of India during COVID-19 lockdown.

Lockdown was imposed by the Indian government in the month of March 2020 as an early precaution to the COVID-19 pandemic which obstructed the socio-economic growth globally. The main aim of this study was to analyse the impact of lockdown (imposed in March and continued in April 2020) on the existing air quality in three megacities of India (Delhi, Mumbai and Kolkata) by assessing the trends of PM10 and NO2 concentrations. A comparison of the percentage reduction in concentrations of lockdown period with respect to same period in year 2019 and pre-lockdown period (February 14-March 24) was made. It was observed from the study that an overall decrease of pollutant concentrations was in the ranges of 30-60% and 52-80% of PM10 and NO2, respectively, in the three cities during lockdown in comparison with previous year and pre-lockdown period. The overall decrease in concentrations of pollutants at urban sites was greater than the background sites. Highest decline in concentrations of PM10 were observed in Kolkata city, followed by Mumbai and Delhi, while decline in NO2 was highest in Mumbai. Results also highlighted that capital city Delhi had the worst air quality amongst three cities, with particulate matter (PM10) being the dominant pollutant. Although COVID-19 has significantly affected the human life considering the mortality and morbidity, lockdowns imposed to control the pandemic had significantly improved the air quality in the selected study locations, although for the short amount of period.

Assessment of spatio-temporal variations in lake water body using indexing method.

The study assesses the characteristics of two lakes located in close vicinity to each other in identifying the status of the lakes based on Designated Best Use (DBU) criteria for optimum utilization and use. Further, the study reports the characteristic assessment of the lakes for two seasons with sampling carried out in August and October months of 2019 and samples collected at different depths and locations to present the comprehensive existing water quality conditions of the lake. The study utilized about twenty parameters evaluated experimentally for determination of Water Quality Indices. In this context, different water quality indices including National Sanitation Foundation Method (NSFWQI) and BIS 10500 (BISWQI) were utilized in determining the indices. The WQI were determined depth wise and a weighted average method was utilized in presenting the overall WQI of the lakes which represents the true water quality based on depth wise evaluation. Hence, the study represents both spatial and temporal variations in the lake water quality. The overall classification of water quality for both the lakes using the NSFWQI methodology was good for both the sampling periods. Similarly, the overall water quality was categorized to be excellent for both the sampling periods using the BISWQI. Further, a new approach in determining water quality indexing is presented through introduction of a Modified Water Quality Index (MWQI) which utilizes the maximum number of parameters and thereby provides a means to reduce ambiguity and eclipsing problems of WQI. Using this newly developed MWQI, the water quality was categorized to be excellent and good for samples collected in August and October respectively for both the lakes. However, conservative estimation considering spillover effects may lead to classification of good category using MWQI. The Heavy Metal Pollution Index (HMI) were classified to be good for both the lakes and sampling periods. Spectral characterization of water samples revealed the presence of oxygen (O), tantalum (Ta), sodium (Na) and Zinc (Zn). However, further monitoring studies are being carried out to cover a period of 1 year to observe if there is a change in water quality due or any seasonal variations.

Occupational Health Hazard Exposure among municipal solid waste workers in Himachal Pradesh, India.

The net volume of Municipal Solid Waste (MSW) generated in Himachal Pradesh is 350 tons per day (TPD) with an annual growth rate varying between 1 and 1.33%. This leads to serious considerations regarding potential occupational health hazards of workers associated with MSW. The study generated from non-engineered landfill sites was carried out at three locations- Shimla, Solan, and Mandi in Himachal Pradesh, India. . The main aim of this study was to determine existing occupational hazards in relation to MSW management at these locations, to coordinate with the different municipalities and suggest suitable remedial measures for our study locations. In particular, the exposure assessment to the work force comprising of street sweeping, waste collection, waste processing, and rag picking were assessed using an interview scheduling technique. A questionnaire survey was carried out on these workers with the questions asked related to their work culture, socio-economic conditions, general awareness of occupational health risks and related occupational health hazards associated with the work being performed by them. The results showed that the workforce mainly comprised of males with a low percentage of literacy rates. The age distribution showed that majority of waste collectors and street sweepers were above 30 years of age (67%) and that the rag pickers in Mandi town were below 20 years of age. Income variation between casual and regular workers was highly significant with regular workers (INR 600-1200; USD 9-18) getting almost six times the payment of the casual workers (INR 100-200; USD 3-7.5). From the studies conducted the study deduced that about 64% of waste collectors, 80% of street sweepers and 10% of rag pickers in Solan and about 6.67% street sweepers, 57.47% waste collectors and 100% of rag pickers in Mandi were not provided with any protective equipment. Interestingly, in the capital city of Shimla a small fraction of the street sweepers (28%) and waste collector (6%) confirmed that they were given protective equipment twice in a year. The lack of provisions of protective equipment along with the ignorance of the workers results in occupational health hazards due to different types of external injuries. The major occupational health issues reported by various categories of waste workers were muscle and ligament sprain, cuts and lacerations and different allergies varying between 1.97 and 66.67% [ for e.g. in Solan it varies from 32.47-66.67%; Shimla 1.97-10.16% and in Mandi 9.52-16.67%], 6.36-67.95% and 5.77-42.86%, respectively. From this study, it was observed that such workers are ill-protected against such occupational health hazards new laws and policies are needed to be introduced for their protection.

Indexing method for assessment of pollution potential of leachate from non-engineered landfill sites and its effect on ground water quality.

Dumping of solid waste in a non-engineered landfill site often leads to contamination of ground water due to leachate percolation into ground water. The present paper assesses the pollution potential of leachate generated from three non-engineered landfill sites located in the Tricity region (one each in cities of Chandigarh, Mohali and Panchkula) of Northern India and its possible effects of contamination of groundwater. Analysis of physico-chemical properties of leachate from all the three landfill sites and the surrounding groundwater samples from five different downwind distances from each of the landfill sites were collected and tested to determine the leachate pollution index (LPI) and the water quality index (WQI). The Leachate Pollution Index values of 26.1, 27 and 27.8 respectively for landfill sites of Chandigarh (CHD), Mohali (MOH) and Panchkula (PKL) cities showed that the leachate generated are contaminated. The average pH values of the leachate samples over the sampling period (9.2 for CHD, 8.97 for MOH and 8.9 for PKL) show an alkaline nature indicating that all the three landfill sites could be classified as mature to old stage. The WQI calculated over the different downwind distances from the contamination sites showed that the quality of the groundwater improved with an increase in the downwind distance. Principal component analysis (PCA) carried out established major components mainly from natural and anthropogenic sources with cumulative variance of 88% for Chandigarh, 87.1% for Mohali and 87.8% for Panchkula. Hierarchical cluster analysis (HCA) identifies three distinct cluster types for the groundwater samples. These clusters corresponds to a relatively low pollution, moderate pollution and high pollution regions. It is suggested that all the three non-engineered landfill sites be converted to engineered landfill sites to prevent groundwater contamination and also new sites be considered for construction of these engineered landfill sites as the present dumpsites are nearing the end of their lifespan capacity.

High resolution spatial and temporal mapping of traffic-related air pollutants.

Vehicle traffic is one of the most significant emission sources of air pollutants in urban areas. While the influence of mobile source emissions is felt throughout an urban area, concentrations from mobile emissions can be highest near major roadways. At present, information regarding the spatial and temporal patterns and the share of pollution attributable to traffic-related air pollutants is limited, in part due to concentrations that fall sharply with distance from roadways, as well as the few monitoring sites available in cities. This study uses a newly developed dispersion model (RLINE) and a spatially and temporally resolved emissions inventory to predict hourly PM2.5 and NOx concentrations across Detroit (MI, USA) at very high spatial resolution. Results for annual averages and high pollution days show contrasting patterns, the need for spatially resolved analyses, and the limitations of surrogate metrics like proximity or distance to roads. Data requirements, computational and modeling issues are discussed. High resolution pollutant data enable the identification of pollutant "hotspots", "project-level" analyses of transportation options, development of exposure measures for epidemiology studies, delineation of vulnerable and susceptible populations, policy analyses examining risks and benefits of mitigation options, and the development of sustainability indicators integrating environmental, social, economic and health information.

Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates.

Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended.

Dispersion Modeling of Traffic-Related Air Pollutant Exposures and Health Effects Among Children with Asthma in Detroit, Michigan.

Vehicular traffic is a major source of ambient air pollution in urban areas. Traffic-related air pollutants, including carbon monoxide, nitrogen oxides, particulate matter less than 2.5 µm in diameter, and diesel exhaust emissions, have been associated with adverse human health effects, especially in areas near major roads. In addition to emissions from vehicles, ambient concentrations of air pollutants include contributions from stationary sources and background (or regional) sources. Although dispersion models have been widely used to evaluate air quality strategies and policies and can represent the spatial and temporal variation in environments near roads, the use of these models in health studies to estimate air pollutant exposures has been relatively limited. This paper summarizes the modeling system used to estimate exposures in the Near-Roadway Exposure and Urban Air Pollutant Study, an epidemiological study that examined 139 children with asthma or symptoms consistent with asthma, most of whom lived near major roads in Detroit, Michigan. Air pollutant concentrations were estimated with a hybrid modeling framework that included detailed inventories of mobile and stationary sources on local and regional scales; the RLINE, AERMOD, and CMAQ dispersion models; and monitored observations of pollutant concentrations. The temporal and spatial variability in emissions and exposures over the 2.5-year study period and at more than 300 home and school locations was characterized. The paper highlights issues with the development and understanding of the significance of traffic-related exposures through the use of dispersion models in urban-scale exposure assessments and epidemiology studies.