Ambient Air Quality and Cardiovascular Health

Cascio, Wayne E.; Long, Thomas

doi:10.18043/ncm.79.5.306

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…These control measures could greatly affect emissions of air pollutants and the air quality ( Wang and Su 2020 ; Xu et al 2020a ; Xu et al 2020c ). It is known that a wide range of adverse health outcomes including asthma, respiratory disease, cardiovascular disease, lung cancer ( Cascio and Long 2018 ; Fuertes et al 2020 ; Guo et al 2019 ; Knibbs et al 2018 ; Rodriguez-Villamizar et al 2015 ; Sun et al 2019 ), etc., are associated with air pollution. While great efforts in recent years have been made to reduce air pollution in particularly fine particulate matter (PM 2.5 ) concentration, China is still suffering relatively heavy air pollution and potentially high health risks particularly in densely populated regions ( Nie et al 2018 ; Sahu et al 2020 ; Shen et al 2017 ; Shen et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Changes of air quality and its associated health and economic burden in 31 provincial capital cities in China during COVID-19 pandemic

Nie

Shen

Wang

et al. 2021

Atmospheric Research

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With outbreak of the novel coronavirus disease (COVID-19), immediate prevention and control actions were imposed in China. Here, we conducted a timely investigation on the changes of air quality, associated health burden and economic loss during the COVID-19 pandemic (January 1 to May 2, 2020). We found an overall improvement of air quality by analyzing data from 31 provincial cities, due to varying degrees of NO 2 , PM 2.5 , PM 10 and CO reductions outweighing the significant O 3 increase. Such improvement corresponds to a total avoided premature mortality of 9410 (7273–11,144) in the 31 cities by comparing the health burdens between 2019 and 2020. NO 2 reduction was the largest contributor (55%) to this health benefit, far exceeding PM 2.5 (10.9%) and PM 10 (23.9%). O 3 instead was the only negative factor among six pollutants. The period with the largest daily avoided deaths was rather not the period with strict lockdown but that during February 25 to March 31, due to largest reduction of NO 2 and smallest increase of O 3 . Southwest, Central and East China were regions with relatively high daily avoided deaths, while for some cities in Northeast China, the air pollution was even worse, therefore could cause more deaths than 2019. Correspondingly, the avoided health economic loss attributable to air quality improvement was 19.4 (15.0–23.0) billion. Its distribution was generally similar to results of health burden, except that due to regional differences in willingness to pay to reduce risks of premature deaths, East China became the region with largest daily avoided economic loss. Our results here quantitatively assess the effects of short-term control measures on changes of air quality as well as its associated health and economic burden, and such information is beneficial to future air pollution control.

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Section: Introductionmentioning

confidence: 99%

Changes of air quality and its associated health and economic burden in 31 provincial capital cities in China during COVID-19 pandemic

Nie

Shen

Wang

et al. 2021

Atmospheric Research

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“…Several air pollutants such as carbon dioxide (CO 2 ), carbon monoxide (CO), particulate matter (PM 2.5 and PM 10 ), sulphur oxides (SO x ), nitrogen dioxide (NO 2 ), and some unburned hydrocarbons can create an unhealthy breathing environment. These pollutants can cause respiratory health issues such as asthma, they can affect the cardiovascular system, and in some cases, they might lead to cancer [ 3 , 4 , 5 ]. Air pollution can be indoor or outdoor; however, people still believe that outdoor air quality (OAQ) has more influence on human health than indoor air quality (IAQ).…”

Section: Introductionmentioning

confidence: 99%

From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model

Kureshi

Thakker

Mishra

et al. 2023

Sensors

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The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditions. There is a pressing need for methodological case studies focusing on informing households about the causes and harms of indoor air pollution and supporting changes in behaviour around different indoor activities that cause it. The use of indoor air quality (IAQ) sensor data to support behaviour change is the focus of our research in this paper. We have conducted two studies—first, to evaluate the effectiveness of the IAQ data visualisation as a trigger for the natural reflection capability of human beings to raise awareness. This study was performed without the scaffolding of a formal behaviour change model. In the second study, we showcase how a behaviour psychology model, COM-B (Capability, Opportunity, and Motivation-Behaviour), can be operationalised as a means of digital intervention to support behaviour change. We have developed four digital interventions manifested through a digital platform. We have demonstrated that it is possible to change behaviour concerning indoor activities using the COM-B model. We have also observed a measurable change in indoor air quality. In addition, qualitative analysis has shown that the awareness level among occupants has improved due to our approach of utilising IoT sensor data with COM-B-based digital interventions.

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“…Deterioration of air quality is an important challenge in most urban parts of the world . Pollutants such as particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO2) can cause respiratory as well as cardiovascular diseases (Cascio and Long 2018;. It is not just adversely affecting human well-being, health and productivity but also overall development and sustainability.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of low-cost sensors for quantitative personal exposure monitoring

Mahajan

Kumar

2020

Sustainable Cities and Society

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Observation of air pollution at high spatio-temporal resolution has become easy with the emergence of low-cost sensors (LCS). LCS provide new opportunities to enhance existing air quality monitoring frameworks but there are always questions asked about the data accuracy and quality. In this study, we assess the performance of LCS against industry-grade instruments. We use linear regression (LR), artificial neural networks (ANN), support vector regression (SVR) and random forest (RF) regression for development of calibration models for LCS, which were Smart Citizen (SC) kits developed in iSCAPE project. Initially, outdoor colocation experiments are conducted where ten SC kits are collocated with GRIMM, which is an industry-grade instrument. Quality check on the LCS data is performed and the data is used to develop calibration models. Model evaluation is done by testing them on 9 SC kits. We observed that the SVR model outperformed other three models for PM2.5 with an average root mean square error of 3.39 and average R 2 of 0.87. Model validation is performed by testing it for PM10 and SVR model shows similar results. The results indicate that SVR can be considered as a promising approach for LCS calibration.

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Ambient Air Quality and Cardiovascular Health

Cited by 7 publications

References 27 publications

Changes of air quality and its associated health and economic burden in 31 provincial capital cities in China during COVID-19 pandemic

Changes of air quality and its associated health and economic burden in 31 provincial capital cities in China during COVID-19 pandemic

From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model

Evaluation of low-cost sensors for quantitative personal exposure monitoring

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