PM2.5 exposure of various microenvironments in a community: Characteristics and applications

Exposure to airborne particulate matter during commuting using portable sensors: Effects of transport modes in a French metropolis study case

Outdoor exposure to particulate matter (PM2.5 and PM10) in urban areas can vary considerably depending on the mode of transport. This study aims to quantify this difference in exposure during daily travel, by carrying out a micro-sensor measurement campaign. The pollutant exposure was assessed simultaneously over predefined routes in order to allow comparison between different transport modes having the same starting and ending points. During the six-week measurement campaign, the average reference values for PM background concentrations were 13.72 and 17.92μg/m3 for the PM2.5 and PM10, respectively. The results revealed that the mode with the highest exposure to PM2.5 adjusted to background concentration (PM2.5Norm) was the bus (1.65) followed by metro (1.51), walking (1.33), tramway (1.31), car (1.09) and finally the bike (1.06). For PM10Norm, the tramway had the highest exposure (1.86), followed by walking (1.68), metro (1.65), bus (1.61), bike (1.43) and finally the car (1.39). The level of urbanization around the route and the presence of preferential lanes for public transportation influenced the concentration to which commuters were exposed. For the active modes (bike and walking), we observed frequent variations in concentrations during the trip, characterized by punctual peaks in concentration, depending on the local characteristics of road traffic and urban morphology. Fluctuations in particulate matter inside public transport vehicles were partly explained by the opening and closing of doors during stops, as well as the passenger flows, influencing the re-suspension of particles. The car was one of the least exposed modes overall, with the lowest concentration variability, although these concentrations can vary greatly depending on the ventilation parameters used. These results encourage measures to move the most exposed users away from road traffic, by developing a network of lanes entirely dedicated to cycling and walking, particularly in densely populated areas, as well as encouraging the renewal of motorized vehicles to use less polluting fuels with efficient ventilation systems.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

The infiltration of wildfire smoke and its potential dose on pregnant women: Lessons learned from Indonesia wildfires in 2019

The occurrence of wildfires in Indonesia is prevalent during drought seasons. Multiple toxic pollutants emitted from wildfires have deleterious effects on pregnant women. However, the evidence for these on pregnant women was underreported. The study conducted 24-h monitoring of fine particulate matter (PM2.5) concentrations indoors and outdoors in 9 low-income homes in Palangka Raya during the 2019 wildfire season and 6 low-income homes during the 2019 non-wildfire season. A hundred and seventy pregnant women had their PM exposure assessed between July and October 2019 using personal monitors. It was observed that outdoor air pollutant levels were greater than those found indoors without indoor sources. The findings indicate that indoor PM2.5 concentrations were modestly increased by 1.2 times higher than outdoor, suggesting that buildings only partially protected people from exposure during wildfires. The concentrations of PM2.5 were found to be comparatively higher indoors in residential buildings with wood material than in brick houses. The study findings indicate that 8 out of 12 brick houses exhibited a notable RI/O24 h of less than 1 during the wildfires, whereas all I/O24 h ratios during the non-wildfire season were >1, suggesting the influence of indoor sources. Based on the estimation of daily PM2.5 dose, pregnant women received around 21% of their total daily dose during sedentary activity involving cooking. The present research offers empirical support for the view that indoor air quality in low-income households is affected by a complex combination of factors, including wildfire smoke, air tightness, and occupant behaviour. Also, this situation is more likely a potential risk to pregnant women being exposed to wildfire smoke.

Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication

People generally spend most of their time indoors, making indoor air quality be of great significance to human health. Large spatiotemporal heterogeneity of indoor air pollution can be hardly captured by conventional filter-based monitoring but real-time monitoring. Real-time monitoring is conducive to change air assessment mode from static and sparse analysis to dynamic and massive analysis, and has made remarkable strides in indoor air evaluation. In this review, the state of art, strengths, challenges, and further development of real-time sensors used in indoor air evaluation are focused on. Researches using real-time sensors for indoor air evaluation have increased rapidly since 2018, and are mainly conducted in China and the USA, with the most frequently investigated air pollutants of PM_2.5. In addition to high spatiotemporal resolution, real-time sensors for indoor air evaluation have prominent advantages in 3-dimensional monitoring, pollution peak and source identification, and short-term health effect evaluation. Huge amounts of data from real-time sensors also facilitate the modeling and prediction of indoor air pollution. However, challenges still remain in extensive deployment of real-time sensors indoors, including the selection, performance, stability, as well as calibration of sensors. In future, sensors with high performance, long-term stability, low price, and low energy consumption are welcomed. Furthermore, more target air pollutants are also expected to be detected simultaneously by real-time sensors in indoor air monitoring.

Personal exposure to PM2.5 in different microenvironments and activities for retired adults in two megacities, China

Microenvironmental concentrations and time-activity patterns influence personal exposure to fine particulate matter (PM_2.5). However, the variations and contributions of PM_2.5 exposures from various microenvironments (MEs) and activities remain unclear. In this study, gravimetrically corrected real-time personal PM_2.5 measurements were collected during routine activities in different MEs from 66 non-smoking retired adults. Exposure data were collected for five consecutive days over two seasons in Nanjing (NJ) and Beijing (BJ), China. Measured PM_2.5 concentrations varied substantially both between and within different MEs and activities. The highest average concentrations were observed in restaurants (NJ: mean 192 μg/m³, SD 242 μg/m³; BJ: mean 91 μg/m³, SD 79 μg/m³) and were associated with sources such as passive smoking and cooking emissions. Overall, PM_2.5 concentrations in different MEs and activities were moderately to highly correlated with outdoor PM_2.5 concentrations (Spearman's r = 0.51-0.97) except in restaurants and during passive smoking. The at-home ME contributed approximately 85 % of the total PM_2.5 exposure, corresponding to the participants spending about 87 % of their time there. The majority of household exposures occurred during sleeping, cooking, and other home-based activities. Transportation accounted for <5 % of total exposure. Our results indicate that improving indoor air quality, especially residential indoors, is important to reduce personal exposure to PM_2.5.

Model development and validation of personal exposure to PM2.5 among urban elders

Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM_2.5 exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM_2.5 exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM_2.5 were conducted for an elderly population in the Taipei city of Taiwan. The proposed microenvironment exposure (ME) models incorporate PM_2.5 measurements and individual time-activity information with a generalized estimating equation (GEE) analysis. We evaluated model performance with daily personal PM_2.5 exposure based on the coefficient of determination, accuracy, and mean bias error. Ambient and home outdoor measures as exposure surrogates are likely to under- and overestimate personal exposure to PM_2.5 in our study population, respectively. Measured and predicted indoor exposures were highly correlated with personal PM_2.5 exposure. The awareness of peculiar smells is an important factor that significantly increases personal PM_2.5 exposure by 46-70%. The model incorporating home indoor PM_2.5 can achieve the highest agreement (R² = 0.790) with personal exposure and the lowest measurement error. The ME model with the GEE analysis combining home outdoor PM_2.5 determined by LUR model with a machine learning technique can improve the prediction (R² = 0.592) of personal PM_2.5 exposure, compared with the prediction of the traditional LUR model (R² = 0.385).

Assessment of the exposure to PM2.5 in different Lebanese microenvironments at different temporal scales

The weak potential of using the sole outdoor concentrations to represent personal exposure to PM_2.5 is confirmed by the literature; therefore, it is important to account for a person's movements over time when estimating the short-term personal air pollution exposure within different microenvironments (MEs). This study is an example of applying an assessment method of the exposure to PM_2.5 in different microenvironments at different temporal scales. A low-cost particle counter (the Dylos 1700) was used; its performance was validated in comparison with equivalent instruments such the SidePak AM520 Personal Aerosol Monitor (R² = 0.89). This validation also provided a function to convert measured particle number concentrations (PNCs) into calculated particle mass concentrations. The 150 profiles that was collected on a minute-by-minute basis regarding PM_2.5 concentration from December 2018 to May 2021 highlight the influence of individual activities and contextual factors on the air quality, so that Lebanon's annual PM_2.5 mean (24.2 µg⁄m³) is 142% higher than the World Health Organization (WHO) annual mean guideline (10 µg⁄m³). Winter is the most polluted period due to the increased application of space heating devices. Additionally, the occurrence of dusty winds during the spring period leads to the elevated levels of dispersed PM_2.5. Simultaneously, the rural zones are more polluted than urban ones due to the usage of more traditional heating equipment, in addition to the usage of chemical products like pesticides and fertilizers in agricultural activities in such areas. Furthermore, the (outdoor-indoor-transport) MEs indicate that the transport and indoor MEs have similar levels of suspended fine particulates, while outdoor MEs are less polluted. Studies based on the personal exposure to PM_2.5 were generally applied on specific and limited places such as schools, workplaces, or residences. The study aims to shed light on the modern method in an attempt to estimate the personal exposure to PM_2.5 and to inspire similar studies to achieve the maximum efficiency.

Estimation of On-Road PM2.5 Distributions by Combining Satellite Top-of-Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

How to reduce the health risks for commuters, caused by air pollution such as PM_2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on-road PM_2.5 throughout the whole city. First, the micro-scale PM_2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat-8 top-of-atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index-based built-up index, are incorporated within the TOA-LUR modeling. On-road PM_2.5 distributions are then mapped in high-spatial-resolution. The maps obtained can be used to find healthy travel routes with less PM_2.5. The proposed framework was applied in high-density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA-LUR Geographical and Temporal Weighted Regression models is at a high-level with an R ² of 0.70-0.90. The newly introduced GVI index played an important role in these estimations. The PM_2.5 distribution maps at high-spatial-resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high-density cities.

A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul

Outdoor concentrations of particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) are often used as a surrogate for population exposure to PM_2.5 in epidemiological studies. However, people spend most of their daily activities indoors; therefore, the relationship between indoor and outdoor PM_2.5 concentrations should be considered in the estimation of population exposure to PM_2.5. In this study, a population exposure model was developed to predict seasonal population exposure to PM_2.5 in Seoul, Korea. The input data for the population exposure model comprised 3984 time-location patterns, outdoor PM_2.5 concentrations, and the microenvironment-to-outdoor PM_2.5 concentrations in seven microenvironments. A probabilistic approach was used to develop the Korea simulation exposure model. The determinants for the population exposure group were identified using a multinomial logistic regression analysis. Population exposure to PM_2.5 varied significantly among the three seasons (p < 0.01). The mean ± standard deviation of population exposures to PM_2.5 was 21.3 ± 4.0 μg/m³ in summer, 9.8 ± 2.7 μg/m³ in autumn, and 29.9 ± 10.6 μg/m³ in winter. Exposure to PM_2.5 higher than 35 μg/m³ mainly occurred in winter. Gender, age, working hours, and health condition were identified as significant determinants in the exposure groups. An "unhealthy" health condition was the most significant determinant. The high PM_2.5 exposure group was characterized as a higher proportion of males of a lower age with longer working hours. The population exposure model for PM_2.5 could be used to implement effective interventions and evaluate the effectiveness of control policies to reduce exposure.

Reduction of aldehyde emission and attribution of environment burden in cooking fumes from food stalls using a novel fume collector

Uncontrolled cooking emissions from commercial kitchens are problematic due to their corresponding health effects and malodors. To reduce cooking emissions, medium and large commercial kitchens install air pollution control devices, such as electrostatic precipitators and wet scrubbers, while small-scale commercial cooking workplaces, such as street-food stalls, use smaller, simpler, and less costly filtration and absorption devices. However, these smaller devices may be poorly designed and recirculate cooking emissions in the workplace. The objectives of this study were to design and implement a novel fume collector and evaluate its effectiveness in reducing aldehydes and the corresponding environmental burden emitted by food stalls. Two stalls, which had malodor problems despite the use of fume collectors, volunteered to participate in the study. To increase the efficiency of the existing fume collectors, a new collector was designed comprising two buckets connected in series, each with pollutant absorption (NaClO-surfactant mixed solution) and particulate filtration (activated-carbon filters) components. Total aldehyde concentrations measured at the exhaust outlets of the original and new collectors were 342.2 and 80.8 μg/m³ for stall A, and 622.7 and 283.1 μg/m³ for stall B, respectively. The corresponding concentration reductions for stall A and B were 76% and 55%, and the emission rate reductions were 91% (from 749 to 71 g/yr) and 76% (from 1040 g/yr to 248 g/h), respectively. These results demonstrate that the effectiveness of the novel collector at removing cooking fumes was significantly improved. The high efficiency and low-cost nature of the collector make it highly applicable in small-scale commercial kitchens and street-food stalls.

Investigation on daily exposure to PM2.5 in Bandung city, Indonesia using low-cost sensor

Daily exposure to PM_2.5 in developing countries has not been thoroughly studied partly due to limited resources available. In this research, personal PM_2.5 exposures in urban communities in Indonesia were examined using a low-cost sensor, AS-LUNG. Fifty subjects were recruited in both wet and dry seasons. Their personal PM_2.5 concentrations, environmental temperature, and relative humidity were measured using corrected AS-LUNG Portable worn or placed in their vicinity. Details on their activities and locations, air quality (air pollution sources), and weather conditions during monitoring were recorded in time-activity diaries completed at 30 min intervals. Results revealed mosquito coil burning as the source of highest exposure, reaching 241.5 μg/m³ but with significant difference between wet and dry seasons. With ambient PM_2.5 and relative humidity controlled for, mosquito coil burning contributed 12.02 μg/m³ and 4.84 μg/m³ of personal PM_2.5 exposure in wet and dry season, respectively, which was several times higher than the contribution from vehicle emission. The second most contributive source was factory smoke, which increased 4.99 μg/m³ and 3.17 μg/m³ of exposure in wet and dry season, respectively. Findings on contributive factors of high daily personal exposures can serve as useful references for formulating policies and recommendations on exposure reduction and health protection.