Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy).

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full "heating season" (15 October 2022-15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = -4.8 and -2.9 µg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 µg/m3) and slightly under-estimated PM10 concentrations (MB = -0.4 µg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68-0.75 vs. 0.59-0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.

Public Health Impacts of Ambient Particulate Matter Pollution in Libya from 1990 to 2019: An Analysis of the 2019 Global Burden of Disease (GBD) Study.

Air pollution is recognized as a critical global health risk, yet there has been no comprehensive assessment of its impact on public health in Libya until now. This study evaluates the burden of disease associated with ambient particulate matter (PM2.5) in Libya, drawing on data from the Global Burden of Disease Study 2019. By integrating satellite-based estimates, chemical transport models, and ground-level measurements, PM2.5 exposure and its effects on mortality and disability-adjusted life years (DALYs) across the different sexes and all age groups from 1990 to 2019 are estimated. Our findings reveal that the annual population-weighted mean PM2.5 concentration in Libya was 38.6 µg/m3 in 2019, marking a 3% increase since 1990. In the same year, PM2.5 was responsible for approximately 3368 deaths, accounting for 11% of all annual deaths in the country. Moreover, a total of 107,207 DALYs were attributable to PM2.5, with ischemic heart disease being the leading cause, representing 46% of these DALYs. The analysis also highlights a significant burden of years of life lost (YLLs) at 89,113 and years lived with disability (YLDs) at 18,094, due to PM2.5. Given the substantial health risks associated with air pollution, particularly from ambient particulate matter, Libyan authorities must implement effective policies aimed at reducing air pollution to enhance healthcare outcomes and preventive services.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment.

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Alternative crop residue management practices to mitigate the environmental and economic impacts of open burning of agricultural residues.

Deliberate open burning of crop residues emits greenhouse gases and toxic pollutants into the atmosphere. This study investigates the environmental impacts (global warming potential, GWP) and economic impacts (net cash flow) of nine agricultural residue management schemes, including open burning, fertilizer production, and biochar production for corn residue, rice straw, and sugarcane leaves. The environmental assessment shows that, except the open burning schemes, fossil fuel consumption is the main contributor of the GWP impact. The fertilizer and biochar schemes reduce the GWP impact including black carbon by 1.88-1.96 and 2.46-3.22 times compared to open burning. The biochar schemes have the lowest GWP (- 1833.19 to - 1473.21 kg CO2-eq/ton). The economic assessment outcomes reveal that the biochar schemes have the highest net cash flow (222.72-889.31 US$2022/ton or 1258.15-13409.16 US$2022/ha). The expenditures of open burning are practically zero, while the biochar schemes are the most costly to operate. The most preferable agricultural residue management type is the biochar production, given the lowest GWP impact and the highest net cash flow. To discourage open burning, the government should tailor the government assistance programs to the needs of the farmers and make the financial assistance more accessible.

Socioeconomic and sociodemographic correlations to COVID-19 variability in the United States in 2020.

The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.

Bioavailability and ecological risk assessment of metal pollutants in ambient PM2.5 in Beijing.

Metal pollutants in fine particulate matter (PM2.5) are physiologically toxic, threatening ecosystems through atmospheric deposition. Biotoxicity and bioavailability are mainly determined by the active speciation of metal pollutants in PM2.5. As a megacity in China, Beijing has suffered severe particulate pollution over the past two decades, and the health effects of metal pollutants in PM2.5 have received significant attention. However, there is a limited understanding of the active forms of metals in PM2.5 and their ecological risks to plants, soil or water in Beijing. It is essential that the ecological risks of metal pollutants in PM2.5 are accurately evaluated based on their bioavailability, identifying the key pollutants and revealing historic trends to future risks control. A two-year project measured the chemical speciation of pollution elements (As, Cd, Cu, Cr, Ni, Mn, Pb, Sb, Sr, Ti, and Zn) in PM2.5 in Beijing, in particular their bioavailability, assessing ecological risks and identifying key pollutants. The mass concentrations of total and active species of pollution elements were 199.12 ng/m3 and 114.97 ng/m3, respectively. Active fractions accounted for 57.7 % of the total. Cd had the highest active proportion. Based on the risk assessment code (RAC), most pollution elements except Ti had moderate or high ecological risk, with RAC exceeding 30 %. Cd, with an RAC of 70 %, presented the strongest ecological risk. Comparing our data with previous research shows that concentrations of pollution elements in PM2.5 in Beijing have decreased over the past decade. However, although the total concentrations of Cd in PM2.5 have decreased by >50 % over the past decade, based on machine model simulation, its ecological risk has reduced by only 10 %. Our research shows that the ecological risks of pollution elements remain high despite their decreasing concentrations. Controlling the active species of metal pollutants in PM2.5 in Beijing in the future is vital.

The effect of fine particulate matter (PM2.5) pollution on health inequality: an intergenerational perspective.

Air pollution poses a serious challenge to public health and simultaneously exacerbating regional & intergenerational health inequality. This research introduces PM2.5 pollution into the intergenerational health transmission model, and estimates its impact on health inequality in China using Ordered Logit Regression (OLR) and Multi-scale Geographically Weighted Regression (MGWR) model. The results indicate that PM2.5 pollution exacerbate the intergenerational health inequality, and its impacts show inconsistency across family income levels, parental health insurance status, and area of residence. Specifically, it is more difficult for offspring in low-income families to escape from the influence of unhealthy family to become upwardly mobile. Additionally, this health inequality is more significant in households in which at least one parent does not have health insurance. Moreover, the intergenerational solidification caused by PM2.5 pollution is higher in the east and lower in the west. Both the PM2.5 level and solidification effect are high in Beijing-Tianjin-Hebei region, Yangtze River Delta region and central areas of China, which is the focus of air pollution management. These findings suggest that more emphasis should be placed on family-based health promotion. In areas with high PM2.5 pollution levels, resources, subsidies and air pollution protection should be provided for less healthy families with lower incomes and no health insurance.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Reducing air pollution does not necessarily reduce related adults' mortality burden: Variations in 177 countries with different economic levels.

Prolonged exposure to PM2.5 is associated with increased mortality. However, reducing air pollution concentrations does not necessarily reduce the related burden of deaths. Here, we aim to estimate the variations in PM2.5-related mortality due to contributions from key factors - PM2.5 concentration, population exposure, and healthcare levels - for 177 countries from 2000 to 2018 at the 1-km grid scale according to the Global Mortality Exposure Model (GEMM) model. We find that global reductions in PM2.5-related deaths mainly come from high and upper-middle income countries, where lowered air pollutant concentration and better healthcare can offset mortality burdens caused by increasing exposed populations. Changes in population exposure to PM2.5 contribute the most (54 %) to change in global related deaths over the examined period, followed by changes in healthcare (-42 %) and pollution concentrations (4 %). The impacts vary across countries and regions within them due to other drivers, which are significantly influenced by development status. Policies aiming at reducing PM2.5 associated health risks need to account for country-specific balances of these key socioeconomic drivers.

Targeting disparate spaces: new technology and old tools.

A growing number of inexpensive, publicly available, validated air quality monitors are currently generating granular and longitudinal data on air quality. The expansion of interconnected networks of these monitors providing open access to longitudinal data represents a valuable data source for health researchers, citizen scientists, and community members; however, the distribution of these data collection systems will determine the groups that will benefit from them. Expansion of these and other exposure measurement networks represents a unique opportunity to address persistent inequities across racial, ethnic, and class lines, if the distribution of these devices is equitable. We present a lean template for local implementation, centered on groups known to experience excess burden of pulmonary disease, leveraging five resources, (a) publicly available, inexpensive air quality monitors connected via Wi-Fi to a centralized system, (b) discharge data from a state hospital repository (c) the U.S. Census, (d) monitoring locations generously donated by community organizations and (e) NIH grant funds. We describe our novel approach to targeting air-quality mediated pulmonary health disparities, review logistical and analytic challenges encountered, and present preliminary data that aligns with a growing body of research: in a high-burden zip code in Durham North Carolina, the census tract with the highest proportions of African Americans experienced worse air quality than a majority European-American census tract in the same zip code. These results, while not appropriate for use in causal inference, demonstrate the potential of equitably distributed, interconnected air quality sensors.

The impact of Chinese foreign direct investment and environmental tax revenues on air degradation in Europe: a spatial regression approach, 2000-2020.

This study analyzes air pollution through the effects of China's FDI in 27 European countries over a 20-year period, with a focus on the impact of environmental tax revenues (ETRs) and the environmental context in China. The relationship is estimated through spatial regressions that account for the presence of air pollutants in neighboring countries. The findings suggest that China's FDI in Europe does not contribute to air pollution but rather has a positive impact. The presence of environmental charges filters out non-polluting investments, which has a non-linear relationship with PM2.5 pollution rates. The study also concludes that air pollution is closely linked to the global environmental context, highlighting the positive effects of international agreements in the fight against climate change. Specifically, the study finds a link between China's efforts to address its polluting activities and their impact on European air quality.

Exploring efficient strategies for air quality improvement in China based on its regional characteristics and interannual evolution of PM2.5 pollution.

Fine particulate matter (PM2.5) harms human health and hinders normal human life. Considering the serious complexity and obvious regional characteristics of PM2.5 pollution, it is urgent to fill in the comprehensive overview of regional characteristics and interannual evolution of PM2.5. This review studied the PM2.5 pollution in six typical areas between 2014 and 2022 based on the data published by the Chinese government and nearly 120 relevant literature. We analyzed and compared the characteristics of interannual and quarterly changes of PM2.5 concentration. The Beijing-Tianjin-Hebei region (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) made remarkable progress in improving PM2.5 pollution, while Fenwei Plain (FWP), Sichuan Basin (SCB) and Northeast Plain (NEP) were slightly inferior mainly due to the relatively lower level of economic development. It was found that the annual average PM2.5 concentration change versus year curves in the three areas with better pollution control conditions can be merged into a smooth curve. Importantly, this can be fitted for the accurate evaluation of each area and provide reliable prediction of its future evolution. In addition, we analyzed the factors affecting the PM2.5 in each area and summarize the causes of air pollution in China. They included primary emission, secondary generation, regional transmission, as well as unfavorable air dispersion conditions. We also suggested that the PM2.5 pollution control should target specific industries and periods, and further research need to be carried out on the process of secondary production. The results provided useful assistance such as effect prediction and strategy guidance for PM2.5 pollution control in Chinese backward areas.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data.

BACKGROUND: Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. METHODS: This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers' exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers' activities. RESULTS: The taxi drivers' weekday and weekend 24-h PM2.5 exposure was 83.60 µg/m3 and 55.62 µg/m3 respectively, 3.4 and 2.2 times than the WHO's recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the "Inner Ring Road", while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the "Third Ring Road". CONCLUSION: These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Quantifying Prescribed-Fire Smoke Exposure Using Low-Cost Sensors and Satellites: Springtime Burning in Eastern Kansas.

Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). The Flint Hills (FH) region of Kansas experiences extensive prescribed burning each spring (March-May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the FH. To determine PM2.5 increases from local burning, we used low-cost PM2.5 sensors (PurpleAir) and satellite observations. The FH were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hr median PM2.5 to increase by 3.0-5.3 µg m-3 (based on different estimates) on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. The FH region was the most impacted by smoke PM2.5 compared to other regions of Kansas, as observed in satellite products and in situ measurements. Additionally, our study found that hourly PM2.5 estimates from a satellite-derived product aligned with our ground-based measurements. Satellite-derived products are useful in rural areas like the FH, where monitors are scarce, providing important PM2.5 estimates.

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China.

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 µm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.

Health impact assessment of PM2.5 from uncovered coal trains in the San Francisco Bay Area: Implications for global exposures.

Coal generates almost 40% of the world's electricity with 80 countries throughout the world using coal power. An inherent part of this generation is the rail transport of coal in uncovered cars, often up to a mile long. Existing studies document the subsequent increments of PM2.5 to the near-rail populations, which typically include a large number of economically disadvantaged residents and/or people of color. However, to date there is no assessment of the health implications of this stage in the use of coal. The present study quantifies such impacts on a region in the San Francisco Bay Area. The analysis shows important effects on mortality, hospitalization for cardiovascular and respiratory disease, asthma exacerbation, work loss, and days of restricted activity. Several of these outcomes exhibited a one to six percent increase over baseline. As such, it delineates the implications for the global effects of the transport of coal.

Is model-estimated PM2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis.

Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.

Assessment of cellular senescence potential of PM2.5 using 3D human lung fibroblast spheroids in vitro model.

Background: Epidemiological studies demonstrate that particulate matter 2.5 (PM2.5) exposure closely related to chronic respiratory diseases. Cellular senescence plays an important role in many diseases. However, it is not fully clear whether PM2.5 exposure could induce cellular senescence in the human lung. In this study, we generated a three-dimensional (3D) spheroid model using isolated primary human lung fibroblasts (HLFs) to investigate the effects of PM2.5 on cellular senescence at the 3D level. Methods: 3D spheroids were exposed to 25-100 µg/ml of PM2.5 in order to evaluate the impact on cellular senescence. SA-ß-galactosidase activity, cell proliferation, and the expression of key genes and proteins were detected. Results: Exposure of the HLF spheroids to PM2.5 yielded a more sensitive cytotoxicity than 2D HLF cell culture. Importantly, PM2.5 exposure induced the rapid progression of cellular senescence in 3D HLF spheroids, with a dramatically increased SA-ß-Gal activity. In exploiting the mechanism underlying the effect of PM2.5 on senescence, we found a significant increase of DNA damage, upregulation of p21 protein levels, and suppression of cell proliferation in PM2.5-treated HLF spheroids. Moreover, PM2.5 exposure created a significant inflammatory response, which may be at least partially associated with the activation of TGF-ß1/Smad3 axis and HMGB1 pathway. Conclusions: Our results indicate that PM2.5 could induce DNA damage, inflammation, and cellular senescence in 3D HLF spheroids, which may provide a new evidence for PM2.5 toxicity based on a 3D model which has been shown to be more in vivo-like in their phenotype and physiology than 2D cultures.

[Characterization of Metal Elements in Atmospheric PM2.5 and Health Risk Assessment in Heze in Winter from 2017 to 2018].

Atmospheric PM2.5 samples were collected in Heze, Shandong Province, from a total of three sampling sites at Heze College, Huarun Pharmacy, and a wastewater treatment plant between October 15, 2017 and January 31, 2018, to determine the concentrations of 21 metal elements in PM2.5 using inductively coupled plasma mass spectrometry (ICP-MS). The degree of elemental enrichment was also discussed, the health risks and potential heavy metal ecological risks were assessed. The results showed that ρ (PM2.5) ranged from 26.7 to 284.1 µg·m-3 at the three sampling sites during the sampling period, and the concentration values did not differ significantly, all of which were at high pollution levels. The highest concentrations of K were found in the three sampling sites, accounting for 31.03%, 39.47%, and 38.43% of the total, respectively, mainly due to the high contribution of biomass burning in autumn and winter in Heze, a large agricultural city. The highest concentrations of Zn, 89.70, 84.21, and 67.68 ng·m-3, were found in the trace elements at the three sampling sites, respectively. The enrichment factor results showed that the enrichment factor values of Zn, Pb, Sn, Sb, Cd, and Se were higher than 100, among which the enrichment factors of Cd and Se were higher than 2 000 and 4 000, respectively, which were significantly influenced by anthropogenic activities and might have been related to industrial production, metal smelting, road sources, and coal combustion emissions. The health risk results showed that there was some potential non-carcinogenic risk (HQ>0.1 for children and adults) for As and a combined potential non-carcinogenic risk (HI>0.1) and some potential carcinogenic risk (CRT>1×10-6) for both children and adults at the three sampling sites. There was a more significant carcinogenic risk (CRT>1×10-4) for adults at the wastewater treatment plant, and the slightly higher carcinogenic risk for adults than that for children may have been related to the longer outdoor activity and higher PM2.5 exposure for adults. The elements with the highest potential ecological risk values were Cd, As, and Pb, with Cd exhibiting a very high potential ecological risk that should be taken seriously. All three sampling sites showed a very high combined potential ecological risk, with the intensity spatially expressed as Heze College>Huarun Pharmacy>wastewater treatment plant.

Multi-scenario PM2.5 distribution and dynamic exposure assessment of university community residents: Development and application of intelligent health risk management system integrated low-cost sensors.

Exposure scenario and receptor behavior significantly affect PM2.5 exposure quantity of persons and resident groups, which in turn influenced indoor or outdoor air quality & health management. An Internet of Things (IoT) system, EnvironMax+, was developed to accurately and conveniently assess residential dynamic PM2.5 exposure state. A university community "QC", as the application area, was divided into four exposure scenarios and five groups of residents. Low-cost mobile sensors and indoor/outdoor pollution migration (IOP) models jointly estimated multi-scenario real-time PM2.5 concentrations. Questionnaire was used to investigate residents' indoor activity characteristics. Mobile application (app) "Air health management (AHM)" could automatic collect residents' activity trajectory. At last, multi-scenario daily exposure concentrations of each residents-group were obtained. The results showed that residential exposure scenario was the most important one, where residents spend about 60 % of their daily time. Closing window was the most significant behavior affecting indoor contamination. The annual average PM2.5 concentration in the studied scenarios: residential scenario (RS) < public scenario (PS) < outdoor scenario (OS) < catering scenario (CS). Except for CS, the outdoor PM2.5 in other scenarios was higher than indoor by 5-10 µg/m3. The multi-scenario population weighted annual average exposure concentration was 37.1 µg/m3, which was 78 % of the annual average outdoor concentration. The exposure concentration of 5 groups: cooks > outdoor workers > indoor workers > students > the elderly, related to their daily activity time proportion in different exposure scenario.