Source Apportionment and Influencing Factor Analysis of Residential Indoor PM2.5 in Beijing

Yang, Yi; Liu, Liu; Xu, Chunyu; Li, Na; Liu, Zhe; Xu, Dongqun

doi:10.3390/ijerph15040686

Cited by 42 publications

(29 citation statements)

References 76 publications

(174 reference statements)

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“…These elements are presumed to be emitted from metals in the boiler and its associated ducting rather than from the fuel itself. Zn is also reported to be released during cooking with gas stoves [ 78 , 79 , 80 , 81 ]. However, in Table 3 , unlike factor 1, the I/O ratio for factor 3, which is slightly lower than 1, indicates that the effects of the outdoor sources are larger than those of the indoor sources.…”

Section: Resultsmentioning

confidence: 99%

PM2.5 and Trace Elements in Underground Shopping Districts in the Seoul Metropolitan Area, Korea

Won

Shim

Kim

et al. 2021

IJERPH

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We measured PM2.5 in 41 underground shopping districts (USDs) in the Seoul metropolitan area from June to November 2017, and associated 18 trace elements to determine the sources and assess the respiratory risks. The PM2.5 concentrations were 18.0 ± 8.0 μg/m3 inside USDs, which were lower than 25.2 ± 10.6 μg/m3 outside. We identified five sources such as indoor miscellanea, soil dust, vehicle exhaust/cooking, coal combustion, and road/subway dust, using factor analysis. Almost 67% of the total trace element concentration resulted from soil dust. Soil dust contribution increased with the number of stores because of fugitive dust emissions due to an increase in passers-by. Vehicle exhaust/cooking contribution was higher when the entrances of the USDs were closed, whereas coal combustion contribution was higher when the entrances of the USDs were open. Although miscellanea and coal combustion contributions were 3.4% and 0.7%, respectively, among five elements with cancer risk, Cr and Ni were included in miscellanea, and Pb, Cd, and As were included in coal combustion. The excess cancer risk (ECR) was the highest at 67 × 10−6 for Cr, and the ECR for Pb was lower than 10−6, a goal of the United States Environmental Protection Agency for hazardous air pollutants.

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Section: Resultsmentioning

confidence: 99%

PM2.5 and Trace Elements in Underground Shopping Districts in the Seoul Metropolitan Area, Korea

Won

Shim

Kim

et al. 2021

IJERPH

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“…The annual average concentrations of heavy metals in PM 2.5 in various areas of Nanjing are shown in Table 2 and Figure 5 and are compared with other domestic and international regions and international air quality guidelines [11,33,34]. The concentrations of atmospheric heavy metals in Nanjing were generally high, especially V and Ti.…”

Section: Seasonal Distribution Of Pm 25 -Bound Heavy Metal Concentramentioning

confidence: 99%

Seasonal Levels, Sources, and Health Risks of Heavy Metals in Atmospheric PM2.5 from Four Functional Areas of Nanjing City, Eastern China

Luo

et al. 2019

Atmosphere

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Aerosol pollution is a serious environmental issue, especially in China where there has been rapid urbanization. To identify the intra-annual and regional distributions of health risks and potential sources of heavy metals in atmospheric particles with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), this work collected monthly PM2.5 samples from urban, industrial, suburban, and rural areas in Nanjing city during 2016 and analyzed the heavy metal compositions (Cu, Pb, Cd, Co, V, Sr, Mn, Ti, and Sb). Enrichment factors (EFs) and principal component analysis (PCA) were applied to investigate the sources. The atmospheric PM2.5 pollution level was highest in the industrial area, followed by the urban and suburban areas, and was the lowest in the rural area. Seasonally, the concentrations of PM2.5 and associated heavy metals in spring and winter were higher than those in summer and autumn. Besides natural sources, heavy metal pollution in PM2.5 might come from metallurgical dust in the industrial area, while it mainly comes from automobile exhaust in urban and suburban areas. Health risk assessments revealed that noncancerous hazards of heavy metals in PM2.5 were low, while the lifetime cancer risks obviously exceeded the threshold. The airborne metal pollution in various functional areas of the city impacted human health differently.

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“…Numerous IoT-based air quality monitoring systems using micro-sensors for data collection have been proposed, including open source technologies for data processing and transmission [18]. According to an IAQ analysis [19], it has been reported that indoor PM 2.5 pollution mainly comes from outdoor sources. In another study, three typical activities such as a having fireplace in the house, cooking with kitchen appliances and toasting were investigated, and indoor pollutant levels such as PM 2.5 , PM 10 and VOC were found to be above the limit values [20].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose

2019

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Today, air pollution is the biggest environmental health problem in the world. Air pollution leads to adverse effects on human health, climate and ecosystems. Air is contaminated by toxic gases released by industry, vehicle emissions and the increased concentration of harmful gases and particulate matter in the atmosphere. Air pollution can cause many serious health problems such as respiratory, cardiovascular and skin diseases in humans. Nowadays, where air pollution has become the largest environmental health risk, the interest in monitoring air quality is increasing. Recently, mobile technologies, especially the Internet of Things, data and machine learning technologies have a positive impact on the way we manage our health. With the production of IoT-based portable air quality measuring devices and their widespread use, people can monitor the air quality in their living areas instantly. In this study, e-nose, a real-time mobile air quality monitoring system with various air parameters such as CO2, CO, PM10, NO2 temperature and humidity, is proposed. The proposed e-nose is produced with an open source, low cost, easy installation and do-it-yourself approach. The air quality data measured by the GP2Y1010AU, MH-Z14, MICS-4514 and DHT22 sensor array can be monitored via the 32-bit ESP32 Wi-Fi controller and the mobile interface developed by the Blynk IoT platform, and the received data are recorded in a cloud server. Following evaluation of results obtained from the indoor measurements, it was shown that a decrease of indoor air quality was influenced by the number of people in the house and natural emissions due to activities such as sleeping, cleaning and cooking. However, it is observed that even daily manual natural ventilation has a significant improving effect on air quality.

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Source Apportionment and Influencing Factor Analysis of Residential Indoor PM2.5 in Beijing

Cited by 42 publications

References 76 publications

PM2.5 and Trace Elements in Underground Shopping Districts in the Seoul Metropolitan Area, Korea

PM2.5 and Trace Elements in Underground Shopping Districts in the Seoul Metropolitan Area, Korea

Seasonal Levels, Sources, and Health Risks of Heavy Metals in Atmospheric PM2.5 from Four Functional Areas of Nanjing City, Eastern China

Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose

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