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

Author: Grafiati

Published: 6 September 2023

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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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5

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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6

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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11

Sehgal, Meena, and Sumit Kumar Gautam. "Odd even story of Delhi traffic and air pollution." International Journal of Environmental Studies 73, no. 2 (March 3, 2016): 170–72. http://dx.doi.org/10.1080/00207233.2016.1153901.

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12

Chhabra, Sunil K., Pragti Chhabra, Sanjay Rajpal, and Rajiv K. Gupta. "Ambient Air Pollution and Chronic Respiratory Morbidity in Delhi." Archives of Environmental Health: An International Journal 56, no. 1 (January 2001): 58–64. http://dx.doi.org/10.1080/00039890109604055.

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13

Prasetya, Tofan Agung Eka, Muhammad Rifki Taufik, Ratnaningtyas Wahyu Kusuma Wardani, Tri Wijayanti Septiarini, and Eka Rosanti. "Characteristics of Ambient Air Pollutions in Delhi, India." Asian Journal of Water, Environment and Pollution 20, no. 3 (May 25, 2023): 1–9. http://dx.doi.org/10.3233/ajw230032.

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Air pollution is characterised as the presence of one or more pollutants in the outdoor environment, such as dust, gases, mist, odour, smoke, or vapour. They are harmful to human, plant, or animal life or property or interfere with the healthy nature of life or property in specific amounts, characteristics, or periods. This study aimed to investigate the characteristics of ambient air pollution through relations between determinants to each SO2, NO2, PM10, and suspended particulate matter (SPM) by applying linear regression. The data has been obtained from the official websites of the Indian government based on the real-time pollutant concentrations monitored by stations in an urban and resident areas from 2000 until 2015. The data consisted of eight (8) variables; SO2, NO2, PM10, and SPM as outcomes, month, year, area, and monitoring stations as determinants. The model showed that the month, year, monitoring station, and area were correlated to SO2, NO2, and PM10 concentration. Yet, in SPM concentration, month, year, the station was correlated. The area was not correlated to SPM. Investigation of other predictors was needed to gain information about the increasing air pollution on a global scale.

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14

Sharma, Arun Kumar, Palak Baliyan, and Prashant Kumar. "Air pollution and public health: the challenges for Delhi, India." Reviews on Environmental Health 33, no. 1 (March 28, 2018): 77–86. http://dx.doi.org/10.1515/reveh-2017-0032.

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AbstractMitigating the impact of pollution on human health worldwide is important to limit the morbidity and mortality arising from exposure to its effect. The level and type of pollutants vary in different urban and rural settings. Here, we explored the extent of air pollution and its impacts on human health in the megacity of Delhi (India) through a review of the published literature. The study aims at describing the extent of air pollution in Delhi, the magnitude of health problems due to air pollution and the risk relationship between air pollution and associated health effects. We found 234 published articles in the PubMed search. The search showed that the extent of air pollution in Delhi has been described by various researchers from about 1986 onwards. We synthesized the findings and discuss them at length with respect to reported values, their possible interpretations and any limitations of the methodology. The chemical composition of ambient air pollution is also discussed. Further, we discuss the magnitude of health problem with respect to chronic obstructive pulmonary diseases (COPD), bronchial asthma and other illnesses. The results of the literature search showed that data has been collected in last 28 years on ambient air quality in Delhi, though it lacks a scientific continuity, consistency of locations and variations in parameters chosen for reporting. As a result, it is difficult to construct a spatiotemporal picture of the air pollution status in Delhi over time. The number of sites from where data have been collected varied widely across studies and methods used for data collection is also non-uniform. Even the parameters studied are varied, as some studies focused on particulate matter ≤10 μm in aerodynamic diameter (PM10) and those ≤2.5 μm in aerodynamic diameter (PM2.5), and others on suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM). Similarly, the locations of data collection have varied widely. Some of the sites were at busy traffic intersections, some on the terraces of offices and residential houses and others in university campuses or airports. As a result, the key question of the extent of pollution and its distribution across various parts of the city could be inferred. None of the studies or a combination of them could present a complete picture of the burden of diseases like COPD, bronchial asthma and other allergic conditions attributable to pollution in Delhi. Neither could it be established what fraction of the burden of the above diseases is attributable to ambient air pollution, given that other factors like tobacco smoke and indoor air pollution are also contributors to the causation of such diseases. In our discussion, we highlight the knowledge gaps and in the conclusion, we suggested what research can be undertaken to fill the these research gaps.

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15

PANDEY, RITA. "Economic policy instruments for controlling vehicular air pollution." Environment and Development Economics 9, no. 1 (January 19, 2004): 47–59. http://dx.doi.org/10.1017/s1355770x03001025.

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When continuous monitoring of individual emissions is not feasible, policy makers need to investigate what other options are available and how best to provide appropriate incentives for pollution reduction. This paper offers an analysis of some such options with a view to identifying and suggesting appropriate policy measures for emission control from automobiles in Delhi.

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16

Bhadauriya, S., N. Chaudhary, S. Mamatha, and S. S. Ray. "RELATIONSHIP BETWEEN RICE RESIDUE BURNING AND INCREASING AIR POLLUTION IN NORTH-WEST INDIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1423–30. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1423-2020.

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Abstract. Punjab and Haryana are two major Rice-producing states of India. They generate high amount of rice residue every year and these residues are burnt in the months of October and November to clear the fields for the next sowing, i.e. Wheat. Residue burning in these two states is considered to be a major factor for the pollution conditions persisting in Delhi, the capital of the country, during October and November. In this study, we aim to analyse the role of stubble burning on Pollution. The approach aimed at a) Determination of rice straw contingent to open burning in the states of Punjab and Haryana, b) Determine and quantify the air pollutant emissions from rice residue contingent to open burning and c) Compare them with the air pollution of Delhi. Also, in order to analyse the various reasons for the increasing pollution in Delhi, Aerosol Parameters like Aerosol Optical Depth, Angstrom Exponent and Single Scattering Albedo were also studied along with auxiliary data like Temperature, Wind Directions, Wind Trajectories, MODIS Fire Counts and CPCB Pollution Data. In this study, we found that not only residue burnings of Punjab and Haryana, but also dust storms from far beyond these states influence the pollution levels in Delhi, especially in the case of Particulate Matter less than 10.

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17

Singh, R., and A. Dewan. "Air conditioners, airborne infection prevention and air pollution in buildings in New Delhi." International Journal of Tuberculosis and Lung Disease 26, no. 3 (March 1, 2022): 288–90. http://dx.doi.org/10.5588/ijtld.21.0704.

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18

Kumar, Raj, Jitendra K. Nagar, Nitin Goel, Pawan Kumar, Alka S. Kushwah, and Shailendra N. Gaur. "Indoor air pollution and asthma in children at Delhi, India." Pneumonologia i Alergologia Polska 83, no. 4 (July 9, 2015): 275–82. http://dx.doi.org/10.5603/piap.2015.0047.

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19

Nongkynrih, Baridalyne, SanjeevKumar Gupta, and SA Rizwan. ""Air pollution in Delhi: Its Magnitude and Effects on Health"." Indian Journal of Community Medicine 38, no. 1 (2013): 4. http://dx.doi.org/10.4103/0970-0218.106617.

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20

Akbar, S., and M. R. Ashmore. "PARTICULATE AIR POLLUTION IN DELHI AND ITS RELATIONSHIPTO RESPIRATORY HEALTH." Epidemiology 7, Supplement (July 1996): S26. http://dx.doi.org/10.1097/00001648-199607001-00032.

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21

Sharma, Nidhi, Shweta Taneja, Vaishali Sagar, and Arshita Bhatt. "Forecasting air pollution load in Delhi using data analysis tools." Procedia Computer Science 132 (2018): 1077–85. http://dx.doi.org/10.1016/j.procs.2018.05.023.

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22

Kumar, Prashant, Mukesh Khare, Roy M. Harrison, William J. Bloss, Alastair C. Lewis, Hugh Coe, and Lidia Morawska. "New directions: Air pollution challenges for developing megacities like Delhi." Atmospheric Environment 122 (December 2015): 657–61. http://dx.doi.org/10.1016/j.atmosenv.2015.10.032.

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23

Goel, Deepti, and Sonam Gupta. "The Effect of Metro Expansions on Air Pollution in Delhi." World Bank Economic Review 31, no. 1 (September 30, 2015): 271–94. http://dx.doi.org/10.1093/wber/lhv056.

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24

Lin, Yinzhen, Qile He, Jiatong Liu, and Jingning Wang. "Is Sharing Mobility a Solution to the Air Pollution Problem: Taking Delhi as an Example." Lecture Notes in Education Psychology and Public Media 5, no. 1 (May 17, 2023): 347–53. http://dx.doi.org/10.54254/2753-7048/5/20220563.

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The sharing economy is a social and economic system built around the sharing of resources. Advances in big data and online platforms in recent years have facilitated the growth of the sharing economy, resulted in some successful sharing model featured with sustainability. In this paper, we analyzed the pollution problem and situation of sharing EV market in Delhi to evaluate the feasibility of applying sharing mobility to the city. We refered to primary literatures and reports about the local condition in Delhi. According to our analysis, sharing mobility is not applicable in Delhi due to its detrimental air pollution and inadequate infrastructure.

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25

Kumar, V. "Landuse Patterns, Air Quality and Bird Diversity in Urban Landscapes of Delhi." Zoodiversity 56, no. 1 (March 25, 2022): 39–50. http://dx.doi.org/10.15407/zoo2022.01.039.

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In the present paper we attempted to explain the relationships among the landuse pattern, levels of air pollutants and bird diversity based on data from 5 sampling sites in Delhi. Five landuse categories- percent built up area, tree cover, park area and barren area were recognized in the study area. The objective of this study is to find out the effects of landuse changes on air pollution and bird diversity and whether birds can serve as indicator of landuse changes and air pollutants. The levels of six air pollutants (PM10, PM2.5, NOX, SO2, Ozone and Benzene) from the monitoring stations were used. The bird diversity was assessed using conventional measures. All the sites showed remarkable differences with respect to each of the five landuse categories, air pollution levels, and bird diversity. The results suggest that landuse changes influence air pollution and bird diversity and some bird species can be used as indicator of landuse change and air pollution.

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26

Dutta, Abhishek, and Wanida Jinsart. "Air pollution in Delhi, India: It’s status and association with respiratory diseases." PLOS ONE 17, no. 9 (September 20, 2022): e0274444. http://dx.doi.org/10.1371/journal.pone.0274444.

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The policymakers need research studies indicating the role of different pollutants with morbidity for polluted cities to install a strategic air quality management system. This study critically assessed the air pollution of Delhi for 2016–18 to found out the role of air pollutants in respiratory morbidity under the ICD-10, J00-J99. The critical assessment of Delhi air pollution was done using various approaches. The mean PM2.5 and PM10 concentrations during the measurement period exceeded both national and international standards by a wide margin. Time series charts indicated the interdependence of PM2.5 and PM10 and connection with hospital visits due to respiratory diseases. Violin plots showed that daily respiratory disease hospital visits increased during the winter and autumn seasons. The winter season was the worst from the city’s air pollution point of view, as revealed by frequency analyses. The single and multi-pollutant GAM models indicated that short-term exposure to PM10 and SO2 led to increased hospital visits due to respiratory diseases. Per 10 units increase in concentrations of PM10 brought the highest increase in hospital visits of 0.21% (RR: 1.00, 95% CI: 1.001, 1.002) at lag0-6 days. This study found the robust effect of SO2 persisted in Delhi from lag0 to lag4 days and lag01 to lag06 days for single and cumulative lag day effects, respectively. While every 10 μg m-3 increase of SO2 concentrations on the same day (lag0) led to 32.59% (RR: 1.33, 95% CI: 1.09, 1.61) rise of hospital visits, the cumulative concentration of lag0-1 led to 37.21% (RR: 1.37, 95% CI:1.11, 1.70) rise in hospital visits which further increased to even 83.33% (RR: 1.83, 95% CI:1.35, 2.49) rise at a lag0-6 cumulative concentration in Delhi. The role of SO2 in inducing respiratory diseases is worrying as India is now the largest anthropogenic SO2 emitter in the world.

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27

Greenstone, Michael, Kenneth Lee, and Harshil Sahai. "Indoor Air Quality, Information, and Socioeconomic Status: Evidence from Delhi." AEA Papers and Proceedings 111 (May 1, 2021): 420–24. http://dx.doi.org/10.1257/pandp.20211006.

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In Delhi, one of the world's most polluted cities, there is relatively little information on indoor air pollution and how it varies by socioeconomic status (SES). Using indoor air quality monitors (IAQMs), we find that winter levels of household air pollution exceed World Health Organization standards by more than 20 times in both high-and low-SES households. We then evaluate a field experiment that randomly assigned monthlong IAQM user trials across medium-and high-SES households but suffered from significant survey non-response. Among respondents, IAQMs did not affect take-up of subsidized air purifier rentals or other defensive behavior.

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28

Kumar, Naresh, and Andrew D. Foster. "Air quality interventions and spatial dynamics of air pollution in Delhi and its surroundings." International Journal of Environment and Waste Management 4, no. 1/2 (2009): 85. http://dx.doi.org/10.1504/ijewm.2009.026886.

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29

Ghazali, Shadab Ahmad, and Raj Kumar. "Predicting Air Pollution in Delhi using Long Short-Term Memory Network." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 482–86. http://dx.doi.org/10.26438/ijcse/v7i5.482486.

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30

Gupta, Suresh K., Viney Gupta, Sujata Joshi, and Radhika Tandon. "Subclinically Dry Eyes in Urban Delhi: An Impact of Air Pollution?" Ophthalmologica 216, no. 5 (2002): 368–71. http://dx.doi.org/10.1159/000066183.

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31

véron, René. "Remaking Urban Environments: The Political Ecology of Air Pollution in Delhi." Environment and Planning A: Economy and Space 38, no. 11 (November 2006): 2093–109. http://dx.doi.org/10.1068/a37449.

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32

Sehgal, Meena, R. Suresh, Ved Prakash Sharma, and Sumit Kumar Gautam. "Assessment of outdoor workers’ exposure to air pollution in Delhi (India)." International Journal of Environmental Studies 72, no. 1 (November 7, 2014): 99–116. http://dx.doi.org/10.1080/00207233.2014.965937.

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33

Pillai, M. K. K. "Pesticide pollution of soil, water and air in Delhi area, India." Science of The Total Environment 55 (November 1986): 321–27. http://dx.doi.org/10.1016/0048-9697(86)90189-0.

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34

Chowdhury, Sourangsu, Sagnik Dey, Sachchida Nand Tripathi, Gufran Beig, Amit Kumar Mishra, and Sumit Sharma. "“Traffic intervention” policy fails to mitigate air pollution in megacity Delhi." Environmental Science & Policy 74 (August 2017): 8–13. http://dx.doi.org/10.1016/j.envsci.2017.04.018.

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35

Kumar, Vijay, Virat Jolli, and Cherukuri Raghvendra Babu. "Avenue plantations in Delhi and their efficacy in mitigating air pollution." Arboricultural Journal 41, no. 1 (January 2, 2019): 35–47. http://dx.doi.org/10.1080/03071375.2019.1562800.

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36

O’Shea, Patrick M., Shouraseni Sen Roy, and R. B. Singh. "Diurnal variations in the spatial patterns of air pollution across Delhi." Theoretical and Applied Climatology 124, no. 3-4 (April 14, 2015): 609–20. http://dx.doi.org/10.1007/s00704-015-1441-y.

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37

Et. al., Shubham Chugh,. "The Impact of Corona-Virus Pandemic Effected the Air Pollution in India." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 203–10. http://dx.doi.org/10.17762/turcomat.v12i2.704.

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: In a country like India, where the major decease are been caused because of the Air pollution, which has affected around 4 million lives because of this pollution only, the cause of the rise in Air pollution is not only from the factories but also the vehicles which use to run on the streets as corona-virus can stay in the air for around 30 minutes, which can cause problems to millions of lives moving on the street, mostly the poor. In India air pollution has rated to almost least in the past 20 years which has contributed to the break from spreading.In India, Delhi and other most populated states stated a drastic downfall in Air pollution with about 60 percent decline in air pollution of PM - 2.5 particularly known as “fine particulate matters” in Delhi when compared with 2019 while the pollution control in other countries couldn’t see much change and which has also seen a rise in corona-virus cases.In this paper we have analyzed the impact of rising in the number of the corona-virus cases concerning the most polluted cities to state the actual scenario that is air pollution leads to a rise in a pandemic situation. This paper is primarily based on secondary sources of data collection including the state-wise downfall in the level of air pollution, impact on the environment from the deadly disease i.e. Corona-virus, prospects, impact on the health of the individual

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38

Talukdar, Shamitaksha, Sachchida Nand Tripathi, Vipul Lalchandani, Maheswar Rupakheti, Himadri Sekhar Bhowmik, Ashutosh K. Shukla, Vishnu Murari, et al. "Air Pollution in New Delhi during Late Winter: An Overview of a Group of Campaign Studies Focusing on Composition and Sources." Atmosphere 12, no. 11 (October 29, 2021): 1432. http://dx.doi.org/10.3390/atmos12111432.

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In recent times, a significant number of studies on the composition and sources of fine particulate matters and volatile organic compounds have been carried out over Delhi, either initiated by or in association with the researchers from the Indian Institute of Technology Kanpur (IIT Kanpur), in collaboration with researchers from within and outside India. All these studies utilized highly time-resolved, campaign-mode observations made with state-of-the-art instrumentation during the late winter months (mid-January to March) of 2018. Individually, each of these studies were rigorous in nature, containing explicit detailing about different types of ambient air pollutants in Delhi such as organic aerosols, inorganic elements, metals, carbonaceous aerosols, and volatile organic compounds. This study consolidates the extremely useful knowledge on source attribution of these air pollutants in the Delhi National Capital Region currently contained in these fragmented studies, which is vital to further enhancing our understanding of composition, characteristics, and sources of air pollutants over Delhi, as well as to designing appropriate mitigation measures tailored to local specifics.

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Chen, Ying, Oliver Wild, Edmund Ryan, Saroj Kumar Sahu, Douglas Lowe, Scott Archer-Nicholls, Yu Wang, et al. "Mitigation of PM<sub>2.5</sub> and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period." Atmospheric Chemistry and Physics 20, no. 1 (January 14, 2020): 499–514. http://dx.doi.org/10.5194/acp-20-499-2020.

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Abstract. Fine particulate matter (PM2.5) and surface ozone (O3) are major air pollutants in megacities such as Delhi, but the design of suitable mitigation strategies is challenging. Some strategies for reducing PM2.5 may have the notable side effect of increasing O3. Here, we demonstrate a numerical framework for investigating the impacts of mitigation strategies on both PM2.5 and O3 in Delhi. We use Gaussian process emulation to generate a computationally efficient surrogate for a regional air quality model (WRF-Chem). This allows us to perform global sensitivity analysis to identify the major sources of air pollution and to generate emission-sector-based pollutant response surfaces to inform mitigation policy development. Based on more than 100 000 emulation runs during the pre-monsoon period (peak O3 season), our global sensitivity analysis shows that local traffic emissions from the Delhi city region and regional transport of pollution emitted from the National Capital Region (NCR) surrounding Delhi are dominant factors influencing PM2.5 and O3 in Delhi. They together govern the O3 peak and PM2.5 concentration during daytime. Regional transport contributes about 80% of the PM2.5 variation during the night. Reducing traffic emissions in Delhi alone (e.g. by 50 %) would reduce PM2.5 by 15 %–20 % but lead to a 20 %–25 % increase in O3. However, we show that reducing NCR regional emissions by 25 %–30 % at the same time would further reduce PM2.5 by 5 %–10 % in Delhi and avoid the O3 increase. This study provides scientific evidence to support the need for joint coordination of controls on local and regional scales to achieve effective reduction in PM2.5 whilst minimising the risk of O3 increase in Delhi.

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Singh, Harikesh, Gowhar Meraj, Sachchidanand Singh, Vaibhav Shrivastava, Vishal Sharma, Majid Farooq, Shruti Kanga, Suraj Kumar Singh, and Pankaj Kumar. "Status of Air Pollution during COVID-19-Induced Lockdown in Delhi, India." Atmosphere 13, no. 12 (December 12, 2022): 2090. http://dx.doi.org/10.3390/atmos13122090.

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To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India’s economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere’s natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World.

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Nidhi and Girija Jayaraman. "Air quality and respiratory health in Delhi." Environmental Monitoring and Assessment 135, no. 1-3 (March 30, 2007): 313–25. http://dx.doi.org/10.1007/s10661-007-9651-0.

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Singh, Yogender, and Umesh Kulshrestha. "An Analysis of GRAP Task Force Directions for Improved AQI in Delhi during 2018." Current World Environment 15, no. 1 (March 23, 2020): 29–41. http://dx.doi.org/10.12944/cwe.15.1.06.

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Air quality has been a matter of public concern in Delhi. The concentration of Particulate Matters (PM2.5 and PM10) often surpasses the Indian National Ambient Air Quality Standards (NAAQS). This study is focused upon the evaluation of Environment Pollution (Prevention and Control) Authority (EPCAs) Graded Response Action Plan (GRAP) action during 2018 in terms of PM2.5 and NO2. In order to control air pollution sources in National Capital Region (NCR), the (EPCA) Environmental Pollution (Prevention and Control) Authority directed GRAP to advise the local industries and other sources of pollution to close their operations on particular dates whenever Air Quality Index (AQI) showed severe level. In this study, we have analyzed 24 hourly averaged Air Quality Index (AQI) data for the period September 2017 - January 2018 and September 2018 - January 2019 at two sites i.e. Delhi Technical University (DTU) and Income Tax Office (ITO) respectively. The GRAP results showed a significant decrease in AQI values of both after every order passed by GRAP task force. In general, the PM2.5 AQI values were always higher during year 2017-18 as compared to 2018-19 at both sites.

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Kumar Gupta, Amit, Aashima Gupta, and Sunita Mahajan. "Air Pollution and Poor Air Quality in Delhi-NCR: Some Health Tips and Protective Measures." Acta Scientific Medical Sciences 3, no. 12 (November 13, 2019): 38–41. http://dx.doi.org/10.31080/asms.2019.03.0469.

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Kumar, Arvaan, and Sriroop Chaudhuri. "Improving Urban Air Quality Monitoring in Delhi, India: Reflections on Low-Cost Air Quality Sensors (LCAQS) and Participatory Engagement." Environment and Urbanization ASIA 13, no. 2 (September 2022): 265–83. http://dx.doi.org/10.1177/09754253221122752.

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In Delhi, the capital city of India, air pollution has been a perpetual menace to urban sustainability and public health. The present study uses a mixed-method approach to enumerate to the urban authorities: (a) the state of air pollution in the city; (b) systemic flaws in the current monitoring network; (c) potential means to bolster it; and (d) need of a participatory framework for monitoring. Information about Air Quality Index (AQI), obtained from 36 monitoring stations across Delhi is compared between 2021 (20 April–25 May; 2nd year/phase of SARS-CoV-2 lockdown), and the corresponding time periods in 2020 (1st year/phase of lockdown), and 2019 (business-as-usual) using the Mann–Whitney U Test. AQI during the 2021 lockdown (a) appeared statistically more similar ( p < .01) to that of 2019 and (b) exceeded the environmental health safety benchmark for 85% days during the study period (20 April–25 May). However, this only presented a partial glimpse into the air pollution status. It owes to numerous ‘holes’ in the AQI data record (no data and/or insufficient data). Moreover, certain areas in Delhi yet have no monitoring station, or only too few, to yield a ‘representative’ estimate (inadequate spatial coverage). Such shortcomings in the existing monitoring network may deter future research and targeted/informed decision-making for pollution control. To that end, the present research offers a summary view of Low-Cost Air Quality Sensors (LCAQS), to offer the urban sustainability authorities, ‘complementary’ technique to bolster and diversify the existing network. The main advantages and disadvantages of various LCAQS sensor technologies are highlighted while emphasizing on the challenges around various calibration techniques (linear and non-linear). The final section reflects on the integration of science and technology with social dimensions of air quality monitoring and highlights key requirements for (a) community mobilization and (b) stakeholder engagement to forge a participatory systems’ design for LCAQS deployment.

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Rooney, Brigitte, Ran Zhao, Yuan Wang, Kelvin H. Bates, Ajay Pillarisetti, Sumit Sharma, Seema Kundu, et al. "Impacts of household sources on air pollution at village and regional scales in India." Atmospheric Chemistry and Physics 19, no. 11 (June 11, 2019): 7719–42. http://dx.doi.org/10.5194/acp-19-7719-2019.

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Abstract. Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM2.5 and O3) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m−3, and, on occasion, exceeded 180 µg m−3. PM2.5 levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM2.5 produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM2.5 during the periods studied at the four locations averaged about 30 µg m−3, representing approximately 30 % and 20 % of total PM2.5 levels in the rural and urban stations, respectively.

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Dutta, Abhishek, Wanida Jinsart, Utpal Chandra Das, and Gautam Dutta. "An empirical analysis of Delhi's air quality throughout different COVID-19 pandemic waves." Journal of Applied and Natural Science 15, no. 1 (March 19, 2023): 325–39. http://dx.doi.org/10.31018/jans.v15i1.4271.

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Delhi was one of India's COVID-19 hotspots, with significant death rates during the year 2021. This study looked at the link between COVID-19 cases in Delhi, and key meteorological variables. The study found that COVID-19 cases during the second wave (P2-March- May 2021) were much higher than during the first wave (P1-Jan-Feb 2021) in Delhi. During P1 (Jan-Feb 2021) the mean PM2.5, PM10, NO2 and CO concentrations were greater than that of P2 (March-May 2021) while the reverse happened for SO2 and O3. Spearman correlation test indicated that COVID-19 cases maintained a significant positive correlation with the high temperature of P2 (March-May 2021) and high humidity of P1 (Jan-Feb 2021) in line with the accepted notion that COVID-19 transmitted favourably in hot and humid climates. The Multilayer perceptron (MLP) model indicated that COVID-19 spread was supported by air pollutants and climate variables like PM2.5, NO2, RH, and WS in P1(Jan-Feb 2021) and PM2.5 and O3 in P2 (March-May 2021). Owing to chemical coupling, across all six monitoring stations, O3 maintained an inverse relationship with NO2 throughout the COVID-19 phases in Delhi. The city dwellers had health risks also due to PM pollution at varying degrees, indicated by high hazard quotients (HQs), requiring lowering of air pollution concentrations on an urgent basis.

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Sikarwar, Ankit, and Ritu Rani. "Assessing the Immediate Effect of Covid-19 Lockdown on Air Quality: A Case Study of Delhi, India." Journal of Environmental Geography 13, no. 3-4 (November 1, 2020): 27–33. http://dx.doi.org/10.2478/jengeo-2020-0009.

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Abstract In India, a nationwide lockdown due to COVID-19 has been implemented on 25 March 2020. The lockdown restrictions on more than 1.3 billion people have brought exceptional changes in the air quality all over the country. This study aims to analyze the levels of three major pollutants: particulate matter sized 2.5 μm (PM2.5) and 10 μm (PM10), and nitrogen dioxide (NO2) before and during the lockdown in Delhi, one of the world’s most polluted cities. The data for PM2.5, PM10, and NO2 concentrations are derived from 38 ground stations dispersed within the city. The spatial interpolation maps of pollutants for two times are generated using Inverse Distance Weighting (IDW) model. The results indicate decreasing levels of PM2.5, PM10, and NO2 concentrations in the city by 93%, 83%, and 70% from 25 February 2020 to 21 April 2020 respectively. It is found that one month before the lockdown the levels of air pollution in Delhi were critical and much higher than the guideline values set by the World Health Organization. The levels of air pollution became historically low after the lockdown. Considering the critically degraded air quality for decades and higher morbidity and mortality rate due to unhealthy air in Delhi, the improvement in air quality due to lockdown may result as a boon for the better health of the city’s population.

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Kumar, Anikender, and Pramila Goyal. "Statistical Downscaling of Air Dispersion Model Using Neural Network for Delhi." Aerosol and Air Quality Research 16, no. 8 (2016): 1879–92. http://dx.doi.org/10.4209/aaqr.2015.06.0384.

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Negi, Chandra Singh, and C. K. Varshney. "Effect of air pollution on photosynthesis-A study of its effect on oxygen evolution." Environment Conservation Journal 4, no. 1-3 (December 22, 2003): 43–50. http://dx.doi.org/10.36953/ecj.2003.0412307.

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This paper deals with the effect of urban air pollution on oxygen evolution by common tropical avenue tree species growing along three important traffic corridors in the capital city of Delhi. Oxygen evolution was measured using Clark-type Hansatech oxygen electrode (U.K.) A marked reduction in oxygen evolution was observed in five species of tropical avenue trees, viz., Azadirachta indica A.Juss, Alstonis scholaris R.Br. Ficus religiosa Linn., Ficus benghalensis Linn. and Morus alba Linn. growing along three important traffic corridors in the capital city of Delhi. Reduction in oxygen evolution was found to be related to the intensity of air pollution resulting from growing automobile traffic. In respect of oxygen evolution, Ficus religiosa was found to be the most sensitive, while Alstonia scholaris was relatively tolerant to roadside automobile pollution. The results of this study suggest that sensitivity of oxygen evolution to air pollution can be an important criteria for selecting avenue trees for road side plantation along high traffic corridors in urban areas and for raising green belts in and around industrial complexes.

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Deshpande, A. "Fuzzy logic in air pollution: Revisited." Nepal Journal of Environmental Science 2 (December 8, 2014): 1–5. http://dx.doi.org/10.3126/njes.v2i0.22735.

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In everyday life and field, people mostly deal with concepts that involve factors that defy classification into crisp sets. The decisions people usually make are perceptions without rigorous analysis of numeric data. Like in other field of studies, there may exist imprecision in air quality parametric data collected and in the perception made by air quality experts in defining these parameters in linguistic terms such as: very good, good, poor. This is the reason why over the past few decades, soft computing tools such as fuzzy logic based methods, neural networks, and genetic algorithms have had significant and growing impacts to deal with aleatory as well as epistemic uncertainty in air quality related issues. This paper has highlighted mathematical preliminaries of air pollution studies like Similarity Measures (Cosine Amplitude Method), Fuzzy to Crisp Conversion (Alpha cut method), Fuzzy c Mean Clustering, Zadeh-Deshpande (ZD) Approach and linguistic description of air quality. Similarly, the applications of fuzzy similarity measures and fuzzy c mean clustering with defined possibility (- cut) levels in case air pollution studies for Delhi, India have been reflected. Though the approach of using fuzzy logic in pollution studies are not of common practice, the comprehensive approach that involves air pollution exposure surveys, toxicological data, and epidemiological studies coupled with fuzzy modeling will go a long way toward resolving some of the divisiveness and controversy in the current regulatory paradigm.

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