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Relevant bibliographies by topics / Air pollution Delhi and Hamirpur

Academic literature on the topic 'Air pollution Delhi and Hamirpur'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Air pollution Delhi and Hamirpur"

1

Rajpoot, Dr Subhadra, and Devang Pratap Singh. "Emerging Public Health Concern and Air Pollution: A Case Study of Delhi’s Air Pollution Governance." International Journal for Modern Trends in Science and Technology 6, no. 5 (May 26, 2020): 196–201. http://dx.doi.org/10.46501/ijmtst060530.

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Air Pollution is a major concern in today’s scenarios as it is leading to serious health hazards and also retrograding our environment. In recent times there has been a rapid increase in various health factors which has affected lives at a very vast scale. Talking about air pollution in cities like Delhi and other metro cities where air pollution is at its peak. Talking about Delhi which is sometimes also referred as ‘Gas Chamber’ has been a research model for managing risk and controlling air pollution in mounting and towards making Delhi's environment healthy. In this research paper we are trying to understand air pollution governance as a means of risk management. Delhi which follows multi-level governance where public health emergencies in recent times, keeping public trust doctrine as the conceptual basis to look at governance. Delhi traversing as National Capital Territory can be considered as a victim of the Air Pollution and its consequent impacts. The lack of integrated approach in Delhi for risk governance has made this process multifaceted and a challenging task. This study can enlighten us on emergence of public health concerns due to air pollution and its governance, keeping in consideration it has not kept an equal balance even with the backing of legislative measures and intervention of court laws. Due to increasing air pollution levels in the city, right to Life and right to a Healthy Environment are being violated from which the levels of air quality continues to be poor. Lastly for which good governance is required in order to reduce the same at this pandemic.

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2

Parveen, Neha, Lubna Siddiqui, Md Nawaj Sarif, Md Safikul Islam, Nazreen Khanam, and Sk Mohibul. "Industries in Delhi: Air pollution versus respiratory morbidities." Process Safety and Environmental Protection 152 (August 2021): 495–512. http://dx.doi.org/10.1016/j.psep.2021.06.027.

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3

The Lancet Planetary Health. "Government indifference over air pollution crisis in Delhi." Lancet Planetary Health 1, no. 9 (December 2017): e348. http://dx.doi.org/10.1016/s2542-5196(17)30165-1.

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4

Jayaraman, Girija, and Nidhi. "Air pollution and associated respiratory morbidity in Delhi." Health Care Management Science 11, no. 2 (January 12, 2008): 132–38. http://dx.doi.org/10.1007/s10729-007-9050-7.

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Sharma, Akriti, Nishtha Hooda, Nidhi Rani Gupta, and Renu Sharma. "Impact of COVID-19 Lockdown on the Risk of Breast Cancer: A Case Study." IOP Conference Series: Earth and Environmental Science 1032, no. 1 (June 1, 2022): 012004. http://dx.doi.org/10.1088/1755-1315/1032/1/012004.

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Abstract Air pollution is among the world’s major environmental concerns. It remains a major health threat in India and is the leading environmental cause of morbidity in the country. There is considerable evidence that heavy and prolonged exposure to several air contaminants increases the cancer risk. The prevalence of breast cancer in citified environments with high exposure to air pollution has been seen to be elevated. Among various Indian cities, the Delhi cancer registry is having a high breast cancer incidence (28.6%). Owing to the recent and unprecedented global outbreak of coronavirus infectious disease (COVID-19), India is exploring every possible way of controlling its vigorous human transmission. Work from home culture is adopted so as to maintain social distancing during the lockdown. This momentary stoppage is substantially reducing the level of air pollution in several city areas across India dramatically. This paper (i) Overviews the breast cancer and air pollution association; (ii) Compiles the air quality data of Delhi monitored by CPCB during the COVID-19 pandemic lockdown time and compares it with pre-lockdown air quality data; (iii) Explores the reduced threat of breast cancer in Delhi during the nationwide lockdown. This work concluded that Air pollution serves a significant part in breast cancer occurrence. The countrywide lockdown in an attempt to prevent Covid-19 transmission has greatly improved the air quality of various Indian cities like Delhi. Also, with an unprecedented drop in rates of air pollution over Delhi, breast cancer occurrence may also decrease.

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Marrapu, P., Y. Cheng, G. Beig, S. Sahu, R. Srinivas, and G. R. Carmichael. "Air quality in Delhi during the CommonWealth Games." Atmospheric Chemistry and Physics Discussions 14, no. 7 (April 17, 2014): 10025–59. http://dx.doi.org/10.5194/acpd-14-10025-2014.

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Abstract. Air quality during The CommonWealth Games (CWG, held in Delhi in October 2010) is analyzed using a new air quality forecasting system established for the Games. The CWG stimulated enhanced efforts to monitor and model air quality in the region. The air quality of Delhi during the CWG had high levels of particles with mean values of PM2.5 and PM10 at the venues of 111 and 238 μg m−3, respectively. Black carbon (BC) accounted for ∼10% of the PM2.5 mass. It is shown that BC, PM2.5 and PM10 concentrations are well predicted, but with positive biases of ∼25%. The diurnal variations are also well captured, with both the observations and the modeled values showing nighttime maxima and daytime minima. A new emissions inventory, developed as part of this air quality forecasting initiative, is evaluated by comparing the observed and predicted species-species correlations (i.e., BC : CO; BC : PM2.5; PM2.5 : PM10). Assuming that the observations at these sites are representative and that all the model errors are associated with the emissions, then the modeled concentrations and slopes can be made consistent by scaling the emissions by: 0.6 for NOx, 2 for CO, and 0.7 for BC, PM2.5 and PM10. The emission estimates for particles are remarkably good considering the uncertainty in the estimates due to the diverse spread of activities and technologies that take place in Delhi and the rapid rates of change. The contribution of various emission sectors including transportation, power, domestic and industry to surface concentrations are also estimated. Transport, domestic and industrial sectors all make significant contributions to PM levels in Delhi, and the sectoral contributions vary spatially within the city. Ozone levels in Delhi are elevated, with hourly values sometimes exceeding 100 ppb. The continued growth of the transport sector is expected to make ozone pollution a more pressing air pollution problem in Delhi. The sector analysis provides useful inputs into the design of strategies to reduce air pollution levels in Delhi. The contribution for sources outside of Delhi on Delhi air quality range from ∼25% for BC and PM to ∼60% for day time ozone. The significant contributions from non-Delhi sources indicates that in Delhi (as has been show elsewhere) these strategies will also need a more regional perspective.

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7

Marrapu, P., Y. Cheng, G. Beig, S. Sahu, R. Srinivas, and G. R. Carmichael. "Air quality in Delhi during the Commonwealth Games." Atmospheric Chemistry and Physics 14, no. 19 (October 9, 2014): 10619–30. http://dx.doi.org/10.5194/acp-14-10619-2014.

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Abstract. Air quality during the Commonwealth Games (CWG, held in Delhi in October 2010) is analyzed using a new air quality forecasting system established for the games. The CWG stimulated enhanced efforts to monitor and model air quality in the region. The air quality of Delhi during the CWG had high levels of particles with mean values of PM2.5 and PM10 at the venues of 111 and 238 μg m−3, respectively. Black carbon (BC) accounted for ~ 10% of the PM2.5 mass. It is shown that BC, PM2.5 and PM10 concentrations are well predicted, but with positive biases of ~ 25%. The diurnal variations are also well captured, with both the observations and the modeled values showing nighttime maxima and daytime minima. A new emissions inventory, developed as part of this air quality forecasting initiative, is evaluated by comparing the observed and predicted species-species correlations (i.e., BC : CO; BC : PM2.5; PM2.5 : PM10). Assuming that the observations at these sites are representative and that all the model errors are associated with the emissions, then the modeled concentrations and slopes can be made consistent by scaling the emissions by 0.6 for NOx, 2 for CO, and 0.7 for BC, PM2.5, and PM10. The emission estimates for particles are remarkably good considering the uncertainty in the estimates due to the diverse spread of activities and technologies that take place in Delhi and the rapid rates of change. The contribution of various emission sectors including transportation, power, domestic and industry to surface concentrations are also estimated. Transport, domestic and industrial sectors all make significant contributions to PM levels in Delhi, and the sectoral contributions vary spatially within the city. Ozone levels in Delhi are elevated, with hourly values sometimes exceeding 100 ppb. The continued growth of the transport sector is expected to make ozone pollution a more pressing air pollution problem in Delhi. The sector analysis provides useful inputs into the design of strategies to reduce air pollution levels in Delhi. The contribution for sources outside of Delhi on Delhi air quality range from ~ 25% for BC and PM to ~ 60% for day time ozone. The significant contributions from non-Delhi sources indicates that in Delhi (as has been show elsewhere) these strategies will also need a more regional perspective.

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8

Ghude, Sachin D., Rajesh Kumar, Gaurav Govardhan, Chinmay Jena, Ravi S. Nanjundiah, and M. Rajeevan. "New Delhi: air-quality warning system cuts peak pollution." Nature 602, no. 7896 (February 8, 2022): 211. http://dx.doi.org/10.1038/d41586-022-00332-y.

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9

Aparnavi, P. "Public Perception Survey on Air Pollution in South Delhi." International Journal of Preventive, Curative & Community Medicine 04, no. 02 (April 7, 2018): 20–27. http://dx.doi.org/10.24321/2454.325x.201812.

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10

Cropper, Maureen L., Nathalie B. Simon, Anna Alberini, Seema Arora, and P. K. Sharma. "The Health Benefits of Air Pollution Control in Delhi." American Journal of Agricultural Economics 79, no. 5 (December 1997): 1625–29. http://dx.doi.org/10.2307/1244393.

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Dissertations / Theses on the topic "Air pollution Delhi and Hamirpur"

1

Akbar, Sameer. "Particulate air pollution and respiratory morbidity in Delhi, India." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268012.

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2

Saraswat, Arvind. "Air pollution in New Delhi, India : spatial and temporal patterns of ambient concentrations and human exposure." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56224.

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Urban air pollution is a major health and environmental concern worldwide, and the levels are extremely high in New Delhi, India. This research is focused on the spatial and temporal variability of air pollutant concentrations and its implications for population exposure in New Delhi. Since traffic is considered a significant source of air pollutants in urban environments, robust and multiple linear regression models were used to understand the impact of local traffic flow on ambient concentrations of PM₂.₅, CO, NO and NO₂ at a busy intersection. To elicit the spatiotemporal variability of PM₂.₅ and its constituents (black carbon and ultrafine particles), land use regression (LUR) models were developed. Separate morning and afternoon models were developed using 136 hours (39 sites), 112 hours (26 sites) and 147 hours (39 sites) of PM₂.₅, BC and UFPN data, respectively. Finally, to understand how spatiotemporal variations in PM₂.₅ concentrations impact population exposure, a probabilistic simulation framework was developed to integrate the PM₂.₅ LUR models with time-activity data obtained from a field survey. Regression models explained about 50–80% variability in hourly pollutant concentrations and localized traffic flow explained up to 19% of variability on that scale. Auto-rickshaw and truck flow had a higher influence on NO₂ and PM₂.₅ concentrations, respectively. Independent variables in the LUR models included population density, distance from major roads, and major and minor road lengths in buffers of different radii; measurements from a fixed continuous monitoring site were also used as independent variables in the PM₂.₅ and BC models. The temporal term explained most of the variability (63–77%) in PM₂.₅ and BC models compared to spatial variables (4–16%). Exposure simulations indicate that the estimated annual average PM₂.₅ exposure (109 µg m-³) was high compared to North American or European cities. PM₂.₅ exposures were highest during the winter months (~200 µg m-³) compared to the summer months (~50 µg m-³). Ignoring mobility (i.e. exposure during transport or at work/school locations), as is generally assumed in epidemiologic studies of long-term exposure, underestimated PM₂.₅ population exposure by about 11%.
Science, Faculty of
Resources, Environment and Sustainability (IRES), Institute for
Graduate

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Books on the topic "Air pollution Delhi and Hamirpur"

1

India. Central Pollution Control Board., ed. Ambient air quality status and trends in Delhi, 1989-1993. Delhi: Central Pollution Control Board, 1997.

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2

Ambient air quality survey at major traffic intersections in Delhi. Delhi: Central Pollution Control Board, 1995.

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3

L, Cropper Maureen, and World Bank. Development Research Group., eds. The health effects of air pollution in Delhi, India. Washington, DC: World Bank, Development Research Group, 1997.

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4

India. Central Pollution Control Board., ed. Ambient noise level and air quality status in Delhi during deepawali festival days 1999-2002. Delhi: Central Pollution Control Board, Ministry of Environment & Forests, Govt. of India, 2003.

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5

Epidemiological study on effect of air pollution on human health (adults) in Delhi. Delhi: PR Division, Central Pollution Control Board, Ministry of Environment & Forests, 2012.

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6

Division, India Central Pollution Control Board PR. Study on ambient air quality, respiratory symptoms and lung function of children in Delhi. Delhi: Central Pollution Control Board, Ministry of Environment & Forests, 2012.

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7

Technical Conference on Air Pollution Control & Monitoring-Issues & Solutions (2012 FICCI). Technical Conference on Air Pollution Control & Monitoring-Issues & Solutions: May 3-4, 2012, FICCI, Federation House, Tansen Marg, New Delhi. New Delhi: Federation of Indian Chambers of Commerce and Industry, 2012.

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8

Alberini, Anna, Maureen Cropper, Nathalie B. Simon, and P. K. Sharma. The Health Effects of Air Pollution in Delhi, India. The World Bank, 1997. http://dx.doi.org/10.1596/1813-9450-1860.

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9

Goel, Deepti, and Sonam Gupta. The Effect of Metro Expansions on Air Pollution in Delhi. Published by Oxford University Press on behalf of the World Bank, 2017. http://dx.doi.org/10.1596/30137.

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10

Urban Green Space, Health Economics and Air Pollution in Delhi. Taylor & Francis Group, 2021.

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Book chapters on the topic "Air pollution Delhi and Hamirpur"

1

Singh, Siddhartha, and S. K. Peshin. "Air Pollution Scenario over Delhi City." In Environment and Sustainable Development, 77–85. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1166-2_6.

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2

Prabhakar, Ashish V. "The air pollution conundrum in Delhi." In Urbanization in the Global South, 225–41. London: Routledge India, 2021. http://dx.doi.org/10.4324/9781003093282-12.

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3

Dholakia, Hem H., and Amit Garg. "Climate Change, Air Pollution and Human Health in Delhi, India." In Climate Change and Air Pollution, 273–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61346-8_17.

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4

Somvanshi, Shivangi Saxena, Aditi Vashisht, Umesh Chandra, and Geetanjali Kaushik. "Delhi Air Pollution Modeling Using Remote Sensing Technique." In Handbook of Environmental Materials Management, 1–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-58538-3_174-1.

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Kumari, Poonam, and Papiya Mandal. "Indoor Air Pollution at Restaurant Kitchen in Delhi NCR." In Lecture Notes in Civil Engineering, 159–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6887-9_18.

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Gulia, Sunil, S. K. Goyal, and Rakesh Kumar. "Air Pollution Episode Analysis and Qualitative Evaluation of Proposed Control Measures in Delhi City." In Urban Air Quality Monitoring, Modelling and Human Exposure Assessment, 225–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5511-4_16.

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Bisht, Manisha, and K. R. Seeja. "Air Pollution Prediction Using Extreme Learning Machine: A Case Study on Delhi (India)." In Proceedings of First International Conference on Smart System, Innovations and Computing, 181–89. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5828-8_18.

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Raman, Ekta, Subhash Anand, I. L. Meitei, Piyush Gupta, and Usha Rani. "Analyzing the Vehicle-Induced Air Pollution and Its Impact in Azadpur Mandi, Delhi." In Sustainable Development Goals Series, 219–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91010-5_17.

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Garnett, Emma, and Srishti Bhatnagar. "Figuring Out Exposure: Exploring Computational Environments and Personalisation in Interdisciplinary Air Pollution Research." In Figure, 197–219. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2476-7_10.

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AbstractAir pollution is often understood through a composition of different kinds of data that increasingly include personal exposure measurements. This chapter is based on our involvement in an interdisciplinary project in Delhi that is combining computational methods and embodied data to simultaneously map, know and respond to air pollution. We use the concept and method of figure, and specifically the figure of ‘the child with asthma’, to explore the tensions that emerge when participants are both objects (sensing bodies that measure air pollution) and subjects (knowing bodies that experience and respond to environmental exposures) of research. This dynamic relationship allows for different ways of figuring out exposure in public health, and thereby possibilities for approaching personalisation that go beyond individualised notions of risk and harm.

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Nagar, Jitendra Kumar, Raj Kumar, J. P. Shrivastava, and Geetanjali Kaushik. "Indoor Air Pollution Around Industrial Areas and Its Effect: A Case Study in Delhi City." In Handbook of Environmental Materials Management, 1–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-58538-3_158-1.

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Conference papers on the topic "Air pollution Delhi and Hamirpur"

1

DE VITO, LAURA, TIM CHATTERTON, ANIL NAMDEO, SHIVA NAGENDRA, SUNIL GULIA, SANJIV GOYAL, MARGARET BELL, et al. "AIR POLLUTION IN DELHI: A REVIEW OF PAST AND CURRENT POLICY APPROACHES." In AIR POLLUTION 2018. Southampton UK: WIT Press, 2018. http://dx.doi.org/10.2495/air180411.

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Kulshreshtha, P., and M. Khare. "A comparative study of indoor air pollution and its respiratory impacts in Delhi, India." In AIR POLLUTION 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/air100251.

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DHYANI, RAJNI, NIRAJ SHARMA, and MUKTI ADVANI. "ESTIMATION OF FUEL LOSS AND SPATIAL–TEMPORAL DISPERSION OF VEHICULAR POLLUTANTS AT A SIGNALIZED INTERSECTION IN DELHI CITY, INDIA." In AIR POLLUTION 2019. Southampton UK: WIT Press, 2019. http://dx.doi.org/10.2495/air190231.

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Khare, M., S. Nagendra, and S. Gulia. "Performance evaluation of air quality dispersion models at urban intersection of an Indian city: a case study of Delhi city." In AIR POLLUTION 2012. Southampton, UK: WIT Press, 2012. http://dx.doi.org/10.2495/air120221.

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Sur, Soumyadeep, Rohit Ghosal, and Rittik Mondal. "Air Pollution Hotspot Identification and Pollution Level Prediction in the City of Delhi." In 2020 IEEE International Conference for Convergence in Engineering (ICCE). IEEE, 2020. http://dx.doi.org/10.1109/icce50343.2020.9290698.

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Srivastava, Chavi, Shyamli Singh, and Amit Prakash Singh. "Estimation of Air Pollution in Delhi Using Machine Learning Techniques." In 2018 International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2018. http://dx.doi.org/10.1109/gucon.2018.8675022.

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Taneja, Shweta, Nidhi Sharma, Kettun Oberoi, and Yash Navoria. "Predicting trends in air pollution in Delhi using data mining." In 2016 1st India International Conference on Information Processing (IICIP). IEEE, 2016. http://dx.doi.org/10.1109/iicip.2016.7975379.

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Singh, Sparsh, Vidit Kumar, Zaid Ahmed, and Kajol Mittal. "Delhi Air Pollution Prediction: A Comparative Analysis using Time Series Forecasting." In 2023 International Conference on Disruptive Technologies (ICDT). IEEE, 2023. http://dx.doi.org/10.1109/icdt57929.2023.10151445.

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Gangamma, S., D. F. Hephzibah, H. S. Vaishnavi, D. Sampada, M. Panigrahi, R. Vishaalini Kamali, V. Veekshitha, and S. K. Varghese. "Air Pollution and Health: Bioaerosols and Reactive Oxygen Species in Delhi City." In American Thoracic Society 2021 International Conference, May 14-19, 2021 - San Diego, CA. American Thoracic Society, 2021. http://dx.doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a3147.

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Khandelwal, Umang, Mayur Shirish Jain, and Kunwar Raghavendra Singh. "Air pollution: A case study on the impact of COVID-19 on Delhi city." In 2ND INTERNATIONAL CONFERENCE ON FUTURISTIC AND SUSTAINABLE ASPECTS IN ENGINEERING AND TECHNOLOGY: FSAET-2021. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0153967.

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