Modeling of residential indoor PM2.5 exposure in 37 counties in China

Particle Size-Dependent Monthly Variation of Pollution Load, Ecological Risk, and Sources of Heavy Metals in Road Dust in Beijing, China

Road dust carries various contaminants and causes urban non-point source pollution in waterbodies through runoff. Road dust samples were collected in each month in two years and then sieved into five particle size fractions. The concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in each fraction were measured. The particle size fraction load index, coefficient of divergence, and Nemerow integrated risk index were used to analyze the temporal variation of pollution load and ecological risk in different particle size fractions. The advanced three-way model and wavelet analysis were used in quantitative identification and time-series analysis of sources. Results showed that both the pollution load and ecological risk of most heavy metals showed a decreasing trend from the finest fraction (P1) to the coarsest fraction (P5). The frequency of heavy metals in P1 posing extreme risk was about two times that of P5. Main types of heavy metal sources were similar among different fractions, whereas the impact intensity of these sources varied among different fractions. Traffic exhaust tended to accumulate in finer particles, and its contribution to Cu in P5 was only 35-55% of that in other fractions. Construction contributed more to coarser particles, and its contribution to Pb was increased from 45.34% in P1 to 65.35% in P5. Wavelet analysis indicated that traffic exhaust showed periodicities of 5-8 and 10-13 months. Fuel combustion displayed the strongest periodicity of 12-15 months, peaking in winter.

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China

Many studies have confirmed that PM2.5 exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM2.5 in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM2.5 data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provides a new research direction. In this study, an advanced machine learning model was constructed using environmental health big data to predict the daily indoor PM2.5 concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R2 value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM2.5 concentrations of the cities ranged from 54.6 μg/m3 to 82.7 μg/m3, and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM2.5 and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM2.5 and contribute to research on the indoor air quality and human health in China. SYNOPSIS: This study established a machine learning model and predicted indoor PM2.5 big data, which could support the research of indoor PM2.5 and health.

Characterization and childhood exposure assessment of toxic heavy metals in household dust under true living conditions from 10 China cities

Health hazards caused by metal exposure in household dust are concerning environmental health problems. Exposure to toxic metals in household dust imposes unclear but solid health risks, especially for children. In this multicenter cross-sectional study, a total of 250 household dust samples were collected from ten stratified cities in China (Panjin, Shijiazhuang, Qingdao, Lanzhou, Luoyang, Ningbo, Xi'an, Wuxi, Mianyang, Shenzhen) between April 2018 and March 2019. Questionnaire was conducted to gather information on individuals' living environment and health status in real-life situations. Multivariate logistic regression and principal component analysis were conducted to identify risk factors and determine the sources of metals in household dust. The median concentration of five metals in household dust from 10 cities ranged from 0.03 to 73.18 μg/g. Among the five heavy metals, only chromium in household dust of Mianyang was observed significantly both higher in the cold season and from the downwind households. Mercury, cadmium, and chromium were higher in the third-tier cities, with levels of 0.08, 0.30 and 97.28 μg/g, respectively. There were two sources with a contribution rate of 38.3 % and 25.8 %, respectively. Potential risk factors for increased metal concentration include long residence time, close to the motorway, decoration within five years, and purchase of new furniture within one year. Under both moderate and high exposure scenarios, chromium showed the highest level of exposure with 6.77 × 10-4 and 2.28 × 10-3 mg·kg-1·d-1, and arsenic imposed the highest lifetime carcinogenic risk at 1.67 × 10-4 and 3.17 × 10-4, respectively. The finding highlighted the priority to minimize childhood exposure of arsenic from household dust.

Substantial health benefits of strengthening guidelines on indoor fine particulate matter in China

In 2020, China for the first time developed guidelines for indoor fine particulate matter (PM_2.5) in the draft document of indoor air standards, while the associated health implication remains unclear. Here, we first estimated the PM_2.5 associated premature deaths was 965 thousand in 2019, with the indoor PM_2.5 of outdoor origin accounting for 72.9%. Then, we examined the dynamic mortalities under a scenario matrix of 36 conditions, by incorporating various shared socioeconomic pathways in 2035, the draft guidelines and the contributions of ambient PM_2.5 to indoor exposure. Although it may be improbable, the averages of premature deaths associated with ambient PM_2.5 will be 1018-1361 thousand in 2035 when the worst-case scenario of guidelines mandating a yearly (rather than daily) indoor PM_2.5 concentration of 75 µg/m³, compared to the averages of estimation were 816-1304 thousand for better-case scenario of 35 µg/m³. Under these scenarios, the increase in the number of premature deaths was mainly driven by population aging. In 2035, an ambitious target of yearly indoor PM_2.5 concentrations of 15 µg/m³ is anticipated to reduce the number of deaths associated with ambient PM_2.5 by approximately 25% of the 2019 baseline. Stricter guidelines to restrict the indoor PM_2.5 concentrations are recommended to mitigate the mortality risk in the future.

Characterization and exposure assessment of household fine particulate matter pollution in China

Household fine particulate matter (PM_2.5 ) pollution greatly impacts residents' health. To explore the current national situation of household PM_2.5 pollution in China, a study was conducted based on literature published from 1998 to 2018. After extracting data from the literature in conformity with the requirements, the nationwide household-weighted mean concentration of household PM_2.5 (HPL) was calculated. Subgroup analyses of spatial, geographic, and temporal differences were also done. The estimated overall HPL in China was 132.2 ± 117.7 μg/m³ . HPL in the rural area (164.3 ± 104.5 μg/m³ ) was higher than that in the urban area (123.9 ± 122.3 μg/m³ ). For HPLs of indoor sampling sites, the kitchen was the highest, followed by the bedroom and living room. There were significant differences of geographic distributions. The HPLs in the South were higher than the North in four seasons. The inhaled dose of household PM_2.5 among school-age children differed from provinces with the highest dose up to 5.9 μg/(kg·d). Countermeasures should be carried out to reduce indoor pollution and safeguard health urgently.

The Synergistic Effect of PM2.5 and CO2 Concentrations on Occupant Satisfaction and Work Productivity in a Meeting Room

High indoor air quality is crucial for the health of human beings. The purpose of this work is to analyze the synergistic effect of particulate matter 2.5 (PM_2.5) and carbon dioxide (CO₂) concentration on occupant satisfaction and work productivity. This study carried out a real-scale experiments in a meeting room with exposures of up to one hour. Indoor environment parameters, including air temperature, relative humidity, illuminance, and noise level, were controlled at a reasonable level. Twenty-nine young participants were participated in the experiments. Four mental tasks were conducted to quantitatively evaluate the work productivity of occupants and a questionnaire was used to access participants' satisfaction. The Spearman correlation analysis and two-way analysis of variance were applied. It was found that the overall performance declined by 1% for every 10 μg/m³ increase in PM_2.5 concentration. Moreover, for every 10% increase in dissatisfaction with air quality, productivity performance decreased by 1.1% or more. It should be noted that a high CO₂ concentration (800 ppm) has a stronger negative effect on occupant satisfaction towards air quality than PM_2.5 concentration in a non-ventilated room. In order to obtain optimal occupant satisfaction and work productivity, low concentrations of PM_2.5 (<50 μg/m³) and CO₂ (<700 ppm) are recommended.

Transfer learning driven sequential forecasting and ventilation control of PM2.5 associated health risk levels in underground public facilities

Particulate matter with aerodynamic diameter less than 2.5 µm (PM_2.5) has become a major public concern in closed indoor environments, such as subway stations. Forecasting platform PM_2.5 concentrations is significant in developing early warning systems, and regulating ventilation systems to ensure commuter health. However, the performance of existing forecasting approaches relies on a considerable amount of historical sensor data, which is usually not available in practical situations due to hostile monitoring environments or newly installed equipment. Transfer learning (TL) provides a solution to the scant data problem, as it leverages the knowledge learned from well-measured subway stations to facilitate predictions on others. This paper presents a TL-based residual neural network framework for sequential forecast of health risk levels traced by subway platform PM_2.5 levels. Experiments are conducted to investigate the potential of the proposed methodology under different data availability scenarios. The TL-framework outperforms the RNN structures with a determination coefficient (R²) improvement of 42.84%, and in comparison, to stand-alone models the prediction errors (RMSE) are reduced up to 40%. Additionally, the forecasted data by TL-framework under limited data scenario allowed the ventilation system to maintain IAQ at healthy levels, and reduced PM_2.5 concentrations by 29.21% as compared to stand-alone network.

Combined use of principal component analysis and artificial neural network approach to improve estimates of PM2.5 personal exposure: A case study on older adults

Accurate exposure estimate of the air pollutant PM_2.5 is required to evaluate its health impacts in epidemiological studies, due to its adverse effects on human's respiratory and cardiovascular systems. However, traditional personal sampling is time and cost consuming. Thus, modeling techniques are needed to accurately predict the personal exposure level to PM_2.5. In this study, a total of 117 older adults over 60 were recruited in Tianjin, a heavily polluted city in northern China, for indoor, outdoor and personal PM_2.5 sampling. Eighteen variables which may increase the exposure level of older adults were recorded for artificial neural network (ANN) simulation. Four modeling techniques, including time-integrated activity modeling, Monte Carlo simulation, ANN modeling, and combined use of principal component analysis (PCA) and ANN model, were used to evaluate their ability for predicting real exposure values of PM_2.5. The results of traditional time-weighted activity modeling showed the lowest correlation with measured values with R² of 0.57 and 0.42 in winter and summer, respectively. For Monte Carlo simulation, high correlation was obtained (R² of 0.93 and 0.92 in winter and summer, respectively) between percentiles of the predicted and the real exposure values. Compared with the simple ANN models, the combined use of PCA and ANN produced the most accurate results with R² of 0.99 and RMSE lower than 15. Since the information of the input variables for the PCA-ANN model can be obtained from the questionnaire and fixed air quality monitoring sites, this technique shows a great potential in predicting personal exposure level to the air pollutant because no additional concentration measurement is needed.

Prediction model for air particulate matter levels in the households of elderly individuals in Hong Kong

Air pollution has shown to cause adverse health effects on mankind. Aging causes functional decline and leaves elderly people more susceptible to health threats associated with air pollution exposure. Elderly spend approximately 80% of their lifetime at home every day. To understand air pollution exposure, indoor air pollutants are the targets for consideration especially for the elderly population. However, indoor air monitoring for epidemiological studies requires a large population, is labor intensive and time consuming. As a result, a prediction model is necessary. For 3 consecutive days in summer and winter, 24-h average of mass concentrations of fine particulate matter (aerodynamic diameter <2.5 μm: PM_2.5) were measured in indoors for 116 households. A PM_2.5 prediction model for elderly households in Hong Kong has been developed by combining ambient PM_2.5 concentrations obtained from land use regression model and questionnaire-elicited information related to the indoor PM_2.5 sources. The fitted linear mixed-effects model is moderately predictive for the observed indoor PM_2.5, with R² = 0.67 (or R² = 0.61 by cross-validation). The model shows indoor PM_2.5 was positively influenced by outdoor PM_2.5 levels. Meteorological factors (e.g. temperature and relative humidity) were related to the indoor PM_2.5 in a relatively complex manner. Congested living areas, opening windows for extended periods for ventilation and use of liquefied petroleum gas for cooking were the factors determining the ultimate indoor air quality. This study aims to provide information about controlling household air quality and can be used for future epidemiological studies associated with indoor air pollution in large population.

Assessment of PM2.5 monitoring using MicroPEM: A validation study in a city with elevated PM2.5 levels

Portable monitors such as MicroPEM can accurately characterize personal exposure of pollutants, which is critical for linking exposure and health effects of air pollution. The RTI (RTI International, Research Triangle Park, NC, USA) MicroPEM V3.2A provides both real-time fine particulate matter (PM_2.5) concentrations and time-integrated PM samples collected onto Teflon filters that can be used to correct real-time data as well as allow further lab chemical analysis of species on filters (e.g., metal, black carbon). Due to the optical reflectivity of local PM sources can be very different from available standard reference particles used for calibration by RTI, there is a need for gravimetric correction and validation at each study location. However, assessments of MicroPEM have been limited in locations with severe air pollution, such as Beijing. We selected a variety of weather and air quality conditions, including both clear and hazy days in Beijing, to compare PM_2.5 data among MicroPEMs as well as between MicroPEM and other types of samplers. We also compared MicroPEM real-time PM_2.5 concentrations with data from nearby fixed-sites. The results show MicroPEM performed well across a wide range of PM_2.5 concentrations (6-461 μg/m³) and MicroPEM data, after gravimetric correction, were consistent with those from moderate-volume samplers. Good agreement was also found between real-time data from MicroPEM and fixed-site data. The present study covered a wide range of pollution levels in actual environments and validated the usage of MicroPEM as a PM_2.5 monitor in locations with elevated PM_2.5 levels.

Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system

Based on 36 road dust samples from an urbanized area of Beijing in September 2016, the information about sources (types, proportions, and intensity in spatial) of heavy metals and uncertainties were analyzed using positive matrix factorization (PMF) model, bootstrap (BS), geographic information system (GIS) and Kriging. The mean concentration of most heavy metals was higher than the corresponding background, and mean concentration of Cd was six times of its background value. Types and proportions of four sources were identified: fuel combustion (33.64%), vehicle emission (25.46%), manufacture and use of metallic substances (22.63%), and use of pesticides, fertilizers, and medical devices (18.26%). The intensity of vehicle emission and the use of pesticides, fertilizers, and medical devices were more homogeneous in spatial (extents were 1.285 and 0.955), while intensity of fuel combustion and the manufacture and use of metallic substances varied largely (extents were 4.172 and 5.518). Uncertainty analysis contained three aspects: goodness of fit, bias and variability in the PMF solution, and impact of input data size. Goodness of fit was assessed by coefficient of determination (R²) of predicted and measured values, and R² of most species were higher than 0.56. Influenced by an outlier, R² of Ni decreased from 0.59 to 0.11. Result of bootstrap (BS) showed good robust of this four-factor configuration in PMF model, and contributions of base run of factors to most species were contained in the small interquartile range and close to median values of bootstrap. Size of input data also had influence on results of PMF model. Residuals changed largely with the increase of number of site, it varied at first and then kept stable after number of site reached 70.

Spatiotemporal Variations of Indoor PM2.5 Concentrations in Nanjing, China

Indoor fine particulate matter (PM2.5) is important since people spend most of their time indoors. However, knowledge of the spatiotemporal variations of indoor PM2.5 concentrations within a city is limited. In this study, the spatiotemporal distributions of indoor PM2.5 levels in Nanjing, China were modeled by the multizone airflow and contaminant transport program (CONTAM), based on the geographically distributed residences, human activities, and outdoor PM2.5 concentrations. The accuracy of the CONTAM model was verified, with a good agreement between the model simulations and measurements (r = 0.940, N = 110). Two different scenarios were considered to examine the building performance and influence of occupant behaviors. Higher PM2.5 concentrations were observed under the scenario when indoor activities were considered. Seasonal variability was observed in indoor PM2.5 levels, with the highest concentrations occurring in the winter and the lowest occurring in the summer. Building characteristics have a significant effect on the spatial distribution of indoor PM2.5 concentrations, with multistory residences being more vulnerable to outdoor PM2.5 infiltration than high-rise residences. The overall population exposure to PM2.5 in Nanjing was estimated. It would be overestimated by 16.67% if indoor exposure was not taken into account, which would lead to a bias in the health impacts assessment.