Characterization of the vertical variation in indoor PM2.5 in an urban apartment in China

Household air pollution disparities between socioeconomic groups in Chicago

Purpose: To assess household air pollution levels in urban Chicago households and examine how socioeconomic factors influence these levels. Methods: We deployed wireless air monitoring devices to 244 households in a diverse population in Chicago to continuously record household fine particulate matter (PM2.5) concentration. We calculated hourly average PM2.5 concentration in a 24-hour cycle. Four factors-race, household income, area deprivation, and exposure to smoking-were considered in this study. Results: A total of 93085 h of exposure data were recorded. The average household PM2.5 concentration was 43.8 μg m-3. We observed a significant difference in the average household PM2.5 concentrations between Black/African American and non-Black/African American households (46.3 versus 31.6 μg m-3), between high-income and low-income households (18.2 versus 52.5 μg m-3), and between smoking and non-smoking households (69.7 versus 29.0 μg m-3). However, no significant difference was observed between households in less and more deprived areas (43.7 versus 43.0 μg m-3). Implications: Household air pollution levels in Chicago households are much higher than the recommended level, challenging the hypothesis that household air quality is adequate for populations in high income nations. Our results indicate that it is the personal characteristics of participants, rather than the macro environments, that lead to observed differences in household air pollution.

Identification of particle distribution pattern in vertical profile via unmanned aerial vehicles observation

Below the boundary layer, the air pollutants have been confirmed to present the decreasing trend with the height in most situaitons. However, the disperiosn rate of air pollutants in the vertical profile is rarely investigated in detail, especially through in-situ measurement. With this consideration, we employed an unmanned aerial vehicle equipped with portable monitoring equipments to scrutinize the vertical distribution of PM2.5. Based on the original data, we found that PM2.5 concentration decreases gradually with altitude below the boundary layer and demonstrated an obvious linear correlation. Therefore, the vertical distribution of PM2.5 was quantified by representing the distribution of PM2.5 with the slope of PM2.5 vertical distribution. We used backward trajectories to reveal the causes of outliers (PM2.5 increasing with altitude), and found that PM2.5 in the high altitude came from the southwest. Besides, the relationship between the vertical distribution of PM2.5 and various meteorological factors was investigated using stepwise regression analysis. The results show that the four meteorological factors most strongly correlated with the slope values are: (a) the difference in relative humidity between the ground and the air; (b) the difference in temperature between the ground and the air; (c) the height of the boundary layer; and (d) the wind speed. The slope values increase with increasing the difference in relative humidity between ground and air and the difference in temperature between the ground and the air, and decrease with increasing boundary layer height and wind speed. According to the Random Forest calculations, the ground-to-air relative humidity difference is the most important at 0.718; the wind speed is the least important at 0.053; and the ground-to-air temperature difference and boundary layer height are 0.140 and 0.088, respectively.

Systematically quantifying the dynamic characteristics of PM2.5 in multiple indoor environments in a plateau city: Implication for internal contribution

People generally spend most of their time indoors, making a comprehensive evaluation of air pollution characteristics in various indoor microenvironments of great significance for accurate exposure estimation. In this study, field measurements were conducted in Kunming City, Southwest China, using real-time PM2.5 sensors to characterize indoor PM2.5 in ten different microenvironments including three restaurants, four public places, and three household settings. Results showed that the daily average PM2.5 concentrations in restaurants, public spaces, and households were 78.4 ± 24.3, 20.1 ± 6.6, and 18.0 ± 4.3 µg/m3, respectively. The highest levels of indoor PM2.5 in restaurants were owing to strong internal emissions from cooking activities. Dynamic changes showed that indoor PM2.5 levels increased during business time in restaurants and public places, and cooking time in residential kitchens. Compared with public places, restaurants generally exhibit more rapid increases in indoor PM2.5 due to cooking activities, which can elevate indoor PM2.5 to high levels (5.1 times higher than the baseline) in a short time. Furthermore, indoor PM2.5 in restaurants were dominated by internal emissions, while outdoor penetration contributed mostly to indoor PM2.5 in public places and household settings. Results from this study revealed large variations in indoor PM2.5 in different microenvironments, and suggested site-specific measures for indoor PM2.5 pollution alleviation.

Source apportionment for indoor air pollution: Current challenges and future directions

Source apportionment (SA) for indoor air pollution is challenging due to the multiplicity and high variability of indoor sources, the complex physical and chemical processes that act as primary sources, sinks and sources of precursors that lead to secondary formation, and the interconnection with the outdoor environment. While the major indoor sources have been recognized, there is still a need for understanding the contribution of indoor versus outdoor-generated pollutants penetrating indoors, and how SA is influenced by the complex processes that occur in indoor environments. This paper reviews our current understanding of SA, through reviewing information on the SA techniques used, the targeted pollutants that have been studied to date, and their source apportionment, along with limitations or knowledge gaps in this research field. The majority (78 %) of SA studies to date focused on PM chemical composition/size distribution, with fewer studies covering organic compounds such as ketones, carbonyls and aldehydes. Regarding the SA method used, the majority of studies have used Positive Matrix Factorization (31 %), Principal Component Analysis (26 %) and Chemical Mass Balance (7 %) receptor models. The indoor PM sources identified to date include building materials and furniture emissions, indoor combustion-related sources, cooking-related sources, resuspension, cleaning and consumer products emissions, secondary-generated pollutants indoors and other products and activity-related emissions. The outdoor environment contribution to the measured pollutant indoors varies considerably (<10 %- 90 %) among the studies. Future challenges for this research area include the need for optimization of indoor air quality monitoring and data selection as well as the incorporation of physical and chemical processes in indoor air into source apportionment methodology.

Toxicological Effects of Secondary Air Pollutants

Secondary air pollutants, originating from gaseous pollutants and primary particulate matter emitted by natural sources and human activities, undergo complex atmospheric chemical reactions and multiphase processes. Secondary gaseous pollutants represented by ozone and secondary particulate matter, including sulfates, nitrates, ammonium salts, and secondary organic aerosols, are formed in the atmosphere, affecting air quality and human health. This paper summarizes the formation pathways and mechanisms of important atmospheric secondary pollutants. Meanwhile, different secondary pollutants' toxicological effects and corresponding health risks are evaluated. Studies have shown that secondary pollutants are generally more toxic than primary ones. However, due to their diverse source and complex generation mechanism, the study of the toxicological effects of secondary pollutants is still in its early stages. Therefore, this paper first introduces the formation mechanism of secondary gaseous pollutants and focuses mainly on ozone's toxicological effects. In terms of particulate matter, secondary inorganic and organic particulate matters are summarized separately, then the contribution and toxicological effects of secondary components formed from primary carbonaceous aerosols are discussed. Finally, secondary pollutants generated in the indoor environment are briefly introduced. Overall, a comprehensive review of secondary air pollutants may shed light on the future toxicological and health effects research of secondary air pollutants.

Indoor air pollution from the household combustion of coal: Tempo-spatial distribution of gaseous pollutants and semi-quantification of source contribution

Coal is a widely used solid fuel for cooking and heating activities in rural households, whose incomplete combustion in inefficient household stoves releases a range of gaseous pollutants. To evaluate the impact of coal combustion on indoor air quality, this study comprehensively investigated the indoor air pollution of typical gaseous pollutants, including formaldehyde (HCHO), carbon dioxide (CO₂), carbon monoxide (CO), total volatile organic compounds (TVOC), and methane (CH₄), during coal combustion process in rural households using online monitoring with high tempo-spatial resolution. The indoor concentrations of gaseous pollutants were considerably elevated during the coal combustion period, with the indoor concentrations being significantly higher than those in courtyard air. The levels of several gaseous pollutants (CO₂, CO, TVOC, and CH₄) in indoor air were much higher during the flaming phase than the de-volatilization and smoldering phases, while HCHO peaked in the de-volatilization phase. The gaseous pollutant concentrations mostly decreased from the room ceiling to the ground level, while their horizontal distribution was relatively uniform within the room. It was estimated that coal combustion accounted for about 71 %, 92 %, 63 %, 59 %, and 21 % of total exposure to indoor CO₂, CO, TVOC, CH₄, and HCHO, respectively. Improved stove combined with clean fuel could effectively lower the concentrations of CO₂, CO, TVOC, and CH₄ in indoor air and reduce the contributions of coal combustion to these gaseous pollutants by about 21-68 %. These findings help better understand the indoor air pollution resulting from residential coal combustion and could guide the development of intervention programs to improve indoor air quality in rural households of northern China.

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.

AlphaMobileSensing: A virtual testbed for mobile environmental monitoring

Environmental monitoring plays a critical role in creating and maintaining a comfortable, productive, and healthy environment. Built upon the advancements of robotics and data processing, mobile sensing demonstrates its potential to address problems regarding cost, deployment, and resolution that stationary monitoring encounters, which therefore has attracted increasing research attentions recently. To facilitate mobile sensing, two key algorithms are needed: the field reconstruction algorithm and the route planning algorithm. The field reconstruction algorithm is to reconstruct the entire environment field from spatially- and temporally-discrete measurements collected by the mobile sensors. The route planning algorithm is to instruct the mobile sensors where the mobile sensor needs to move to for the next measurements. The performance of mobile sensors highly depends on these two algorithms. However, developing and testing those algorithms in the real world is expensive, challenging, and time-consuming. To address these issues, we proposed and implemented an open-source virtual testbed, AlphaMobileSensing, that can be used to develop, test, and benchmark mobile sensing algorithms. AlphaMobileSensing aims to help users more easily develop and test the field reconstruction and route planning algorithms for mobile sensing solutions, without worrying about hardware fault, test accidents (such as collision during the test), etc. The separation of concerns can significantly reduce the cost of developing software solutions for mobile sensing. For versatility and flexibility, AlphaMobileSensing was wrapped up using the standardized interface of OpenAI Gym, and it also provides an interface for loading physical fields that were generated by numerical simulations as virtual test sites to perform mobile sensing and retrieving monitoring data. We demonstrated applications of the virtual testbed by implementing and testing algorithms for physical field reconstruction in both static and dynamic indoor thermal environments. AlphaMobileSensing provides a novel and flexible platform to develop, test, and benchmark mobile sensing algorithms more easily, conveniently, and efficiently. AlphaMobileSensing is open sourced at https://github.com/kishuqizhou/AlphaMobileSensing.

Electronic Supplementary Material ESM

the Appendix is available in the online version of this article at 10.1007/s12273-023-1001-9.