scholarly journals Novel index to comprehensively evaluate air cleanliness: the Clean aIr Index (CII)

2020 ◽  
Vol 3 (2) ◽  
pp. 233-247
Author(s):  
Tomohiro O. Sato ◽  
Takeshi Kuroda ◽  
Yasuko Kasai
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
City Planning ◽  
Air Pollutant ◽  
World Health ◽  
Observation System ◽  
In Situ Observation ◽  
Validation Data ◽  
Air Index ◽  
Clean Air

Abstract. Air quality on our planet has been changing in particular since the industrial revolution (1750s) because of anthropogenic emissions. It is becoming increasingly important to realize air cleanliness, since clean air is as valuable a resource as clean water. A global standard for quantifying the level of air cleanliness is swiftly required, and we defined a novel concept, namely the Clean aIr Index (CII). The CII is a simple index defined by the normalization of the amount of a set of individual air pollutants. A CII value of 1 indicates completely clean air (no air pollutants), and 0 indicates the presence of air pollutants that meet the numerical environmental criteria for the normalization. In this time, the air pollutants used in the CII were taken from the Air Quality Guidelines (AQG) set by the World Health Organization (WHO), namely O3, particulate matters, NO2, and SO2. We chose Japan as a study area to evaluate CII because of the following reasons: (i) accurate validation data, as the in situ observation sites of the Atmospheric Environmental Regional Observation System (AEROS) provide highly accurate values of air pollutant amounts, and (ii) fixed numerical criteria from the Japanese Environmental Quality Standards (JEQS) as directed by the Ministry of the Environment (MOE) of Japan. We quantified air cleanliness in terms of the CII for the all 1896 municipalities in Japan and used data from Seoul and Beijing to evaluate Japanese air cleanliness. The amount of each air pollutant was calculated using a model that combined the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models for 1 April 2014 to 31 March 2017. The CII values calculated by the WRF–CMAQ model and the AEROS measurements showed good agreement. The mean of the correlation coefficient for the CII values of 498 municipalities where the AEROS measurements operated was 0.66±0.05, which was higher than that of the Air Quality Index (AQI) of 0.57±0.06. The CII values averaged for the study period were 0.67, 0.52, and 0.24 in Tokyo, Seoul, and Beijing, respectively; thus, the air in Tokyo was 1.5 and 2.3 times cleaner, i.e., lower amounts of air pollutants, than the air in Seoul and Beijing, respectively. The average CII value for the all Japanese municipalities was 0.72 over the study period. The extremely clean air, CII ≈0.90, occurred in the southern remote islands of Tokyo and to the west of the Pacific coast, i.e., Kochi, Mie, and Wakayama prefectures during summer, with the transport of clean air from the ocean. We presented the top 100 clean air cities in Japan as one example of an application of CII in society. We confirmed that the CII enabled the quantitative evaluation of air cleanliness. The CII can be useful and valuable in various scenarios such as encouraging sightseeing and migration, investment and insurance business, and city planning. The CII is a simple and fair index that can be applied to all nations.

scholarly journals Novel index to comprehensively evaluate air cleanness: the Clean aIr Index

2019 ◽  
Author(s):  
Tomohiro O. Sato ◽  
Takeshi Kuroda ◽  
Yasuko Kasai
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Air Pollutant ◽  
World Health ◽  
Observation System ◽  
In Situ Observation ◽  
Insurance Company ◽  
Validation Data ◽  
Air Index ◽  
Clean Air

Abstract. Air quality on our planet has been changing in particular since the industrial revolution (1750s) because of anthropogenic emissions. It is becoming increasingly important to visualize air cleanness, since clean air deserves a valuable resource as clean water. We defined a novel concept, namely Clean aIr Index, CII, to quantify the level of air cleanness in terms of a global standard. The CII is a simple index defined by the normalization of the amount of individual air pollutants. A CII value of 1 indicates completely clean air (no air pollutants), and 0 indicates the presence of air pollutants up to numerical environmental criteria for the normalization. In this time, the air pollutants used in the CII were taken from the Air Quality Guidelines (AQG) set by the World Health Organization (WHO), namely O3, particulate matters, NO2 and SO2. We chose Japan as a study area to evaluate CII because of the following reasons: i) accurate validation data, as the in situ observation sites of the Atmospheric Environmental Regional Observation System (AEROS) provide highly accurate values of air pollutant amounts, ii) obvious numerical criteria, namely the Japanese Environmental Quality Standards given by the Ministry of the Environment (MOE). We quantified air cleanness in terms of the CII for the all 1896 municipalities in Japan, and used Seoul and Beijing to evaluate Japanese air cleanness. The amount of each air pollutant was calculated using a model that combined the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models for 1 April 2014 to 31 March 2017. The CII values were validated by comparing the WRF-CMAQ model and AEROS measurements for selected six cities, and an average correlation coefficient of >0.61 was obtained. The CII value of Tokyo averaged for the study period was 0.75, which was 1.2 and 1.9 times higher than that of Seoul (0.64) and Beijing (0.39), respectively. The extremely clean air, CII > 0.93, occurred around west of the Pacific coast, i.e., Kochi, Mie and Wakayama Prefectures, and southern remote islands of Tokyo during summer with transport of clean air from the ocean. The average CII value for the all Japanese municipalities was 0.78 over the study period. We presented Top 100 clean air cities in Japan using the CII. We confirmed that the CII enabled the quantitative evaluation of air cleanness. The CII can be useful value, for example, for encouraging sightseeing and migration, as tasty air, insurance company business, and city planning. The CII is a simple and fair index that can be applied to all nations.

scholarly journals Long-Term Assessment of Air Quality and Identification of Aerosol Sources at Setúbal, Portugal

2020 ◽  
Vol 17 (15) ◽  
pp. 5447 ◽  
Author(s):  
Alexandra Viana Silva ◽  
Cristina M. Oliveira ◽  
Nuno Canha ◽  
Ana Isabel Miranda ◽  
Susana Marta Almeida
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Traffic ◽  
Anthropogenic Sources ◽  
World Health ◽  
Mitigation Measures ◽  
Guideline Values ◽  
Health Organization

Understanding air pollution in urban areas is crucial to identify mitigation actions that may improve air quality and, consequently, minimize human exposure to air pollutants and their impact. This study aimed to assess the temporal evolution of the air quality in the city of Setúbal (Portugal) during a time period of 10 years (2003–2012), by evaluating seasonal trends of air pollutants (PM10, PM2.5, O3, NO, NO2 and NOx) measured in nine monitoring stations. In order to identify emission sources of particulate matter, PM2.5 and PM2.5–10 were characterized in two different areas (urban traffic and industrial) in winter and summer and, afterwards, source apportionment was performed by means of Positive Matrix Factorization. Overall, the air quality has been improving over the years with a decreasing trend of air pollutant concentration, with the exception of O3. Despite this improvement, levels of PM10, O3 and nitrogen oxides still do not fully comply with the requirements of European legislation, as well as with the guideline values of the World Health Organization (WHO). The main anthropogenic sources contributing to local PM levels were traffic, industry and wood burning, which should be addressed by specific mitigation measures in order to minimize their impact on the local air quality.

scholarly journals Tougher Targets for China’s Clean Air Cities? Implications from Air Quality Assessment in Shenzhen

2018 ◽  
Author(s):  
Dian Huang ◽  
Qinglan Li ◽  
Guangxin Li ◽  
Xiaoxue Wang ◽  
Liqun Sun
Keyword(s):  
Quality Improvement ◽  
Air Quality ◽  
Air Pollutants ◽  
Quality Standards ◽  
Ambient Air ◽  
Quality Standard ◽  
World Health ◽  
Air Quality Standards ◽  
Air Quality Improvement ◽  
Clean Air

Shenzhen is China’s top ten clean air city and the cleanest air megacity. Even so, epidemiologic studies have shown ambient air pollution had significant adverse impacts on human health in this less polluted city. In this study, the concentrations of six criteria air pollutants (PM2.5, PM10, O3, NO2, SO2, and CO) from 2014 to 2017 were analyzed and compared to thresholds of both national and international air quality standards. The results showed concentrations of all air pollutants were below target values of current national air quality standard, but levels of particulate matter (PM) and O3 were still much higher than the recommended levels by the World Health Organization. Within national air quality standards, the number of over-limit days was rare with few variations between highly polluted and low pollution areas. The air quality improvement was slowing down recently. Our results suggest annual and daily thresholds for PM are too loose for air quality improvement in Shenzhen. Hence, we call for evaluation and establishment of tougher air quality standard.

scholarly journals How Is Indoor Air Quality during Sleep? A Review of Field Studies

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 110
Author(s):  
Nuno Canha ◽  
Catarina Teixeira ◽  
Mónica Figueira ◽  
Carolina Correia
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Pollutants ◽  
Human Exposure ◽  
Real Life ◽  
Air Pollutant ◽  
World Health ◽  
Limit Values ◽  
Life Conditions

This review aimed to provide an overview of the characterisation of indoor air quality (IAQ) during the sleeping period, based only on real life conditions’ studies where, at least, one air pollutant was considered. Despite the consensual complexity of indoor air, when focusing on sleeping environments, the available scientific literature is still scarce and falls to provide a multipollutants’ characterisation of the air breathed during sleep. This review, following PRISMA’s approach, identified a total of 22 studies that provided insights of how IAQ is during the sleeping period in real life conditions. Most of studies focused on carbon dioxide (77%), followed by particles (PM2.5, PM10 and ultrafines) and only 18% of the studies focused on pollutants such as carbon monoxide, volatile organic compounds and formaldehyde. Despite the high heterogeneity between studies (regarding the geographical area, type of surrounding environments, season of the year, type of dwelling, bedrooms’ ventilation, number of occupants), several air pollutants showed exceedances of the limit values established by guidelines or legislation, indicating that an effort should be made in order to minimise human exposure to air pollutants. For instance, when considering the air quality guideline of World Health Organisation of 10 µg·m−3 for PM2.5, 86% of studies that focused this pollutant registered levels above this threshold. Considering that people spend one third of their day sleeping, exposure during this period may have a significant impact on the daily integrated human exposure, due to the higher amount of exposure time, even if this environment is characterised by lower pollutants’ levels. Improving the current knowledge of air pollutants levels during sleep in different settings, as well as in different countries, will allow to improve the accuracy of exposure assessments and will also allow to understand their main drivers and how to tackle them.

scholarly journals Gender Differences in Association between Air Pollution and Daily Mortality in the Capital of the Green Lungs of Poland–Population-Based Study with 2,953,000 Person-Years of Follow-Up

2020 ◽  
Vol 9 (8) ◽  
pp. 2351
Author(s):  
Łukasz Kuźma ◽  
Krzysztof Struniawski ◽  
Szymon Pogorzelski ◽  
Hanna Bachórzewska-Gajewska ◽  
Sławomir Dobrzycki
Keyword(s):  
Public Health ◽  
Gender Differences ◽  
Air Pollution ◽  
Air Quality ◽  
Air Pollutant ◽  
World Health ◽  
The European Union ◽  
Daily Mortality ◽  
Male Population ◽  
The Impact

(1) Introduction: air pollution is considered to be one of the main risk factors for public health. According to the European Environment Agency (EEA), air pollution contributes to the premature deaths of approximately 500,000 citizens of the European Union (EU), including almost 5000 inhabitants of Poland every year. (2) Purpose: to assess the gender differences in the impact of air pollution on the mortality in the population of the city of Bialystok—the capital of the Green Lungs of Poland. (3) Materials and Methods: based on the data from the Central Statistical Office, the number—and causes of death—of Białystok residents in the period 2008–2017 were analyzed. The study utilized the data recorded by the Provincial Inspectorate for Environmental Protection station and the Institute of Meteorology and Water Management during the analysis period. Time series regression with Poisson distribution was used in statistical analysis. (4) Results: A total of 34,005 deaths had been recorded, in which women accounted for 47.5%. The proportion of cardiovascular-related deaths was 48% (n = 16,370). An increase of SO2 concentration by 1-µg/m3 (relative risk (RR) 1.07, 95% confidence interval (CI) 1.02–1.12; p = 0.005) and a 10 °C decrease of temperature (RR 1.03, 95% CI 1.01–1.05; p = 0.005) were related to an increase in the number of daily deaths. No gender differences in the impact of air pollution on mortality were observed. In the analysis of the subgroup of cardiovascular deaths, the main pollutant that was found to have an effect on daily mortality was particulate matter with a diameter of 2.5 μm or less (PM2.5); the RR for 10-µg/m3 increase of PM2.5 was 1.07 (95% CI 1.02–1.12; p = 0.01), and this effect was noted only in the male population. (5) Conclusions: air quality and atmospheric conditions had an impact on the mortality of Bialystok residents. The main air pollutant that influenced the mortality rate was SO2, and there were no gender differences in the impact of this pollutant. In the male population, an increased exposure to PM2.5 concentration was associated with significantly higher cardiovascular mortality. These findings suggest that improving air quality, in particular, even with lower SO2 levels than currently allowed by the World Health Organization (WHO) guidelines, may benefit public health. Further studies on this topic are needed, but our results bring questions whether the recommendations concerning acceptable concentrations of air pollutants should be stricter, or is there a safe concentration of SO2 in the air at all.

scholarly journals Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

2020 ◽  
Author(s):  
Shibao Wang ◽  
Yun Ma ◽  
Zhongrui Wang ◽  
Lei Wang ◽  
Xuguang Chi ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Mitigation Strategies ◽  
Traffic Emissions ◽  
Urban Air ◽  
Industrial Emissions ◽  
Gis Technology ◽  
Mobile Monitoring

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyper-local scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (Oct. 2019–Sep. 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. While the O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

2021 ◽  
Author(s):  
Adrian Wenzel ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Sebastian T. Thekkekara ◽  
Daniel Zollitsch ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Sensor Network ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Small Scale ◽  
Urban Air

<p>Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution – especially with respect to spatial and temporally variability – measurement data with high spatial and temporal resolution are critical.</p><p>Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O<sub>3</sub>) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].</p><p>After having conducted a measurement campaign in 2016 to create a high-resolution NO<sub>2</sub> concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O<sub>3</sub> and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.</p><p> </p><p>[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018</p><p>[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018</p><p>[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, 2020</p><p>[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020</p>

FRI0531 AIR POLLUTANTS AND DEVELOPMENT OF INTERSTITIAL LUNG DISEASE IN PATIENTS WITH AUTOIMMUNE DISEASES

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 865.1-865
Author(s):  
H. H. Chen ◽  
W. C. Chao ◽  
Y. H. Chen ◽  
D. Y. Chen ◽  
C. H. Lin
Keyword(s):  
Air Quality ◽  
Autoimmune Diseases ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Air Quality Monitoring ◽  
Newly Diagnosed ◽  
Science Technology ◽  
Air Quality Monitoring Stations ◽  
The Impact ◽  
Modest Level

Background:Interstitial lung disease (ILD) is characterized by progressive inflammation and fibrosis, and accumulating evidence have shown that exposure to air pollutants was associated with the development of ILD. Autoimmune diseases are highly correlated with ILD, including connective tissue disease-associated ILD (CTD-ILD) as well as interstitial pneumonia with autoimmune features (IPAF), and the development of ILD is a crucial cause of morbidity and mortality in patients with autoimmune diseases. One recent Taiwanese study reported that exposure to air pollutants was associated with incident systemic lupus erythematosus (SLE). However, the impact of air pollutants on the development of ILD among patients with autoimmune diseases remains unknown.Objectives:The study aimed to address the impact of accumulating exposure to air pollutant above moderate level, defined by Air Quality Index (AQI) value higher than 50, on the development of ILD in patients with autoimmune diseases including SLE, rheumatoid arthritis (RA) and primary Sjögren’s syndrome (SS).Methods:We used a National Health Insurance Research Database in Taiwan to enroll patients with SLE (International Classification of Diseases (ICD)-9 code 710.0, n=13,211), RA (ICD-9 code 714.0 and 714.30–714.33, n=32,373), and primary SS (ICD-9 code, 710.0, n=15,246) between 2001 and 2013. We identified newly diagnosed ILD cases (ICD-code 515) between 2012 and 2013 and selected age, sex, disease duration and index-year matched (1:4) patients as non-ILD controls. The hourly levels of air pollutants one year prior to the index-date were obtained from 60 air quality monitoring stations across Taiwan, and the air pollutants in the present study consisted of particulate matter <2.5 μm in size (PM2.5), particulate matter <10 μm in size (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2) and ozone (O3). We used a spatio-temporal model built by a deep-learning mechanism to estimate levels of air pollutants at 374 residential locations based on data of 3 air quality monitoring stations near the location (8). Notably, we used cumulative exposed hours to air pollutants higher than modest level, defined by AQI criteria, given that daily mean level of air pollutants might possibly underestimate the triggered inflammatory effect by a temporary exposure of high-level air pollutant. A conditional logistic regression was used to determine the association between exposure to air pollutant and the development of ILD, adjusting age, gender, Charlson Comorbidity Index (CCI), urbanization, family income, and medications for autoimmune diseases.Results:A total of 272 patients with newly diagnosed ILD were identified among patients with autoimmune diseases, including 39 with SLE, 135 with RA, and 98 with primary SS. We found that the duration of exposure to PM 2.5 higher than modest level was associated with the risk of ILD development in patients with SS (adjOR 1.07, 95% CI 1.01–1.13), and similar trends were also found in patients with SLE (adjOR 1.03, 95% CI 0.95–1.12) and RA (adjOR 1.03, 95% CI 0.99–1.07). Intriguingly, we observed an inverse correlation between the duration of exposure to O3 and the development of ILD in patients with SS (adjOR 0.83, 95% CI 0.70–0.99); however, the finding was not found in patients with SLE (adjOR 1.13, 95% CI 0.92–1.37) and RA (adjOR 0.98, 95% CI 0.87–1.11).Conclusion:In conclusion, we identified that longer exposure to PM2.5 higher than modest level tended to be associated with the development of ILD in patients with autoimmune diseases, mainly SS.References:[1] Araki T, Putman RK, Hatabu H, Gao W, Dupuis J, Latourelle JC, et al. Development and Progression of Interstitial Lung Abnormalities in the Framingham Heart Study. Am J Respir Crit Care Med 2016;194:1514-1522.[2] Tang KT, Tsuang BJ, Ku KC, Chen YH, Lin CH, Chen DY. Relationship between exposure to air pollutants and development of systemic autoimmune rheumatic diseases: a nationwide population-based case-control study. Ann Rheum Dis 2019;78:1288-1291.Disclosure of Interests:Hsin-Hua Chen: None declared, Wen-Cheng Chao: None declared, Yi-Hsing Chen Grant/research support from: Taiwan Ministry of Science and Technology, Taiwan Department of Health, Taichung Veterans General Hospital, National Yang-Ming University, GSK, Pfizer, BMS., Consultant of: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Paid instructor for: Pfizer, Novartis, Johnson & Johnson, Roche, Lilly, Astra& Zeneca, Sanofi, Astellas, Agnitio Science Technology, United Biopharma., Speakers bureau: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Der-Yuan Chen: None declared, Ching-Heng Lin: None declared

scholarly journals Poor Air Quality and Its Association with Mortality in Ho Chi Minh City: Case Study

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 750
Author(s):  
Hoang Ngoc Khue Vu ◽  
Quang Phuc Ha ◽  
Duc Hiep Nguyen ◽  
Thi Thu Thuy Nguyen ◽  
Thoai Tam Nguyen ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Ambient Air ◽  
Air Quality Modeling ◽  
World Health ◽  
Ho Chi Minh City ◽  
Annual Average ◽  
Quality Modeling ◽  
Concentration Of Air Pollutants

Along with its rapid urban development, Ho Chi Minh City (HCMC) in recent years has suffered a high concentration of air pollutants, especially fine particulate matters or PM2.5. A comprehensive study is required to evaluate the air quality conditions and their health impact in this city. Given the lack of adequate air quality monitoring data over a large area of the size of HCMC, an air quality modeling methodology is adopted to address the requirement. Here, by utilizing a corresponding emission inventory in combination with The Air Pollution Model-Chemical Transport Model (TAPM-CTM), the predicted concentration of air pollutants is first obtained for PM2.5, NOx, and SO2. Then by associating the pollutants exposed with the mortality rate from three causes, namely Ischemic Heart Disease (IHD), cardiopulmonary, and lung cancer, the impact of air pollution on human health is obtained for this purpose. Spatial distribution has shown a high amount of pollutants concentrated in the central city with a high density of combustion vehicles (motorcycles and automobiles). In addition, a significant amount of emissions can be observed from stevedoring and harbor activities, including ferries and cargo handling equipment located along the river. Other sources such as household activities also contribute to an even distribution of emission across the city. The results of air quality modeling showed that the annual average concentrations of NO2 were higher than the standard of Vietnam National Technical Regulation on Ambient Air Quality (QCVN 05: 2013 40 µg/m3) and World Health Organization (WHO) (40 µg/m3). The annual average concentrations of PM2.5 were 23 µg/m3 and were also much higher than the WHO (10 µg/m3) standard by about 2.3 times. In terms of public health impacts, PM2.5 was found to be responsible for about 1136 deaths, while the number of mortalities from exposure to NO2 and SO2 was 172 and 89 deaths, respectively. These figures demand some stringent measures from the authorities to potentially remedy the alarming situation of air pollution in HCM City.

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