Ambient air pollutant monitoring and analysis protocol for low and middle income countries: An element of comprehensive urban air quality management framework

Gulia, Sunil; Khanna, Isha; Shukla, K. K.; Khare, Mukesh

doi:10.1016/j.atmosenv.2019.117120

Cited by 42 publications

(21 citation statements)

References 48 publications

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“…However, each different area shows different noise level since there are various factors both from industrial activities and other human activities. Several studies in scientific publications on air quality have been reported [19][20][21]. Where the results show that the average benzene level in ambient air in Asian countries (371μg/m 3 ) was about 3.5 times higher than the level of benzene in the room (111μg/m 3 ).…”

Section: Resultsmentioning

confidence: 97%

Analysis of Air, Water and Noise Level Quality Due to Industrials Activities in Aceh Province

Zaki

Mahidin

Mariana

et al. 2020

ARFMTS

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Air pollution throughout the world, in general, has been at a very alarming rate with the increase in industrial and human activities. In Indonesia, the level of air pollution caused by various industries, especially from motor vehicles, is no longer a secret. Particularly, in the province of Aceh, air, water, and air noise quality in recent years have resulted in deteriorating health for humans and living things. This research analyzed air quality, water odor, and air noise level in the Aceh Province. The results for air quality analysis show an increase in emissions. The noise level of the three regions analyzed is still below the thresholds set. Meanwhile, the results of water odor analysis show its level has exceeded the thresholds set by the Ministry of Environment. Therefore, industrial activities can have an impact on the environment when they are not managed appropriately.

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Section: Resultsmentioning

confidence: 97%

Analysis of Air, Water and Noise Level Quality Due to Industrials Activities in Aceh Province

Zaki

Mahidin

Mariana

et al. 2020

ARFMTS

View full text Add to dashboard Cite

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“…The highest exposure rates of ambient air pollution (i.e. forest fires, dust storms, industrial and traffic exhausts [44]) are seen in developing countries [48]. In the last 10 years, a significant increase in DALYs attributed to ambient air pollution has been documented in areas with a low-and lowmiddle socio-demographic index.…”

Section: Environmental Risk Factors: Air Pollutionmentioning

confidence: 99%

Strategies for the prevention, diagnosis and treatment of COPD in low- and middle- income countries: the importance of primary care

Rossaki

Hurst

Gemert

et al. 2021

Expert Review of Respiratory Medicine

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Introduction: Low-and middle-income countries (LMICs) bear a high proportion of the global morbidity and mortality caused by COPD. Increased exposure to risk factors throughout life (e.g. malnutrition, indoor and outdoor air pollution, and smoking) is associated with higher COPD prevalence in LMICs and the lack of treatment availability increases avoidable harm. Areas covered: This review covers the epidemiology and burden of COPD in LMICs, and challenges and recommendations related to health-care systems, prevention, diagnosis, and treatment. Main challenges are related to under-resourced health-care systems (such as limited availability of spirometry, rehabilitation, and medicines). Lack of policy and practical local guidelines on COPD diagnosis and management further contribute to the low diagnostic and treatment rates. In the absence of, or limited number of respiratory specialists, primary care practitioners (general practitioners, nurses, pharmacists, physiotherapists, and community health workers) play an even more pivotal role in COPD management in LMICs. Expert opinion: Raising awareness on COPD, educating health-care workers, patients, and communities on cost-effective preventive measures as well as improving availability, affordability and proper use of diagnostic and pharmacological and non-pharmacologic treatment in primary care are the key interventions needed to improve COPD prevention, diagnosis, and care in LMICs.

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“…Researchers have provided many references for the government to adjust environmental policies in a timely manner by analyzing the impact of pollutant discharge on the environment [10]. In order to quantify the air environmental policy, Chalabi et al [11] established a corresponding system framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Data Based Regression to Monitor Air Pollutant Emission in Factories

Gao

2021

Sustainability

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Air pollution originating from anthropogenic emission, which is an important factor for environmental policy to regulate the sustainable development of enterprises and the environment. However, the missing or mislabeled discharge data make it impossible to apply this strategy in practice. In order to solve this challenge, we firstly discover that the energy consumption in a factory and the air pollutants are linearly related. Given this observation, we propose a support vector regression based Single-location recovery model to recover the air pollutant emission by using the energy consumption data in a factory. To further improve the precision of air pollutant emission estimation, we proposed a Gaussian process regression based multiple-location recovery model to estimate and recover the missing or mislabeled air pollutant emission from surrounding available air quality readings, collected by the government’s air quality monitoring station. Moreover, we optimally combine the two approaches to achieve the accurate air air pollutant emission estimation. To our best of knowledge, this is the first paper for monitoring the air pollutant emission taking both a factory’s energy consumption and government’s air quality readings into account. The research model in this article uses actual data(10,406,880 entries of data including weather, PM 2.5, date, etc.) from parts of Shandong Province, China. The dataset contains 33 factories (5 types) and we use the co-located air quality monitoring station as ground truth. The results show that, our proposed single-location recovery, multi-location recovery, and combined method could acquire the mean absolute error of 8.45, 9.69, and 7.25, respectively. The method has consistent accurate prediction behavior among 5 different factory types, shows a promising potential to be applied in broader locations and application areas, and outperforms the existing spatial interpolation based methods by 43.8%.

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Ambient air pollutant monitoring and analysis protocol for low and middle income countries: An element of comprehensive urban air quality management framework

Cited by 42 publications

References 48 publications

Analysis of Air, Water and Noise Level Quality Due to Industrials Activities in Aceh Province

Analysis of Air, Water and Noise Level Quality Due to Industrials Activities in Aceh Province

Strategies for the prevention, diagnosis and treatment of COPD in low- and middle- income countries: the importance of primary care

Multimodal Data Based Regression to Monitor Air Pollutant Emission in Factories

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