Characterization of PM2.5 exposure concentration in transport microenvironments using portable monitors

High spatial resolution estimates of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach

Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85-0.96 and 0.62-0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies.

Relative contributions of ambient air and internal sources to multiple air pollutants in public transportation modes

Commuters are often exposed to relatively high air pollutant concentrations in public transport microenvironments (TMEs) because of their proximity to emission sources. Previous studies have mainly focused on assessing the concentrations of air pollutants in TMEs, but few studies have distinguished between the contributions of ambient air and internal sources to the exposure of commuters to air pollutants. The main objective of this study was to quantify the contributions of ambient air and internal sources to the measured particulate matter and gaseous pollutant concentrations in selected TMEs in Hong Kong, a high-rise, high-density city in Asia. A sampling campaign was conducted to measure air pollutant concentrations in TMEs in Hong Kong in July and November 2018 using portable air quality monitors. We measured the concentrations of each pollutant in different TMEs and quantified the infiltration of particulate matter into these TMEs. The double-decker bus had the lowest particulate matter concentrations (mean PM1, PM2.5, and PM10 concentrations of 5.1, 9.5, and 13 μg/m3, respectively), but higher concentrations of CO (0.9 ppm), NO (422 ppb), and NO2 (100 ppb). For all the TMEs, about half of the PM2.5 were PM1 particles. The Mass Transit Railway (MTR) subway system had a PM2.5/PM10 ratio of about 0.90, whereas the PM2.5/PM10 ratio was about 0.60-0.70 for the other TMEs. The MTR had infiltration factor estimates <0.4 for particulate matter, lower than those of the double-decker bus and minibus. The MTR had the highest contribution from internal sources (mean PM1, PM2.5, and PM10 concentrations of 4.6, 13.4, and 15.8 μg/m3, respectively). This study will help citizens to plan commuting routes to reduce their exposure to air pollution and help policy-makers to prioritize effective exposure reduction strategies.

A review of personal exposure studies in selected Asian countries' public transport microenvironments: lessons learned and future directions

This comprehensive paper conducts an in-depth review of personal exposure and air pollutant levels within the microenvironments of Asian city transportation. Our methodology involved a systematic analysis of an extensive body of literature from diverse sources, encompassing a substantial quantity of studies conducted across multiple Asian cities. The investigation scrutinizes exposure to various pollutants, including particulate matters (PM10, PM2.5, and PM1), carbon dioxide (CO2), formaldehyde (CH2O), and total volatile organic compounds (TVOC), during transportation modes such as car travel, bus commuting, walking, and train rides. Notably, our review reveals a predominant focus on PM2.5, followed by PM10, PM1, CO2, and TVOC, with limited attention given to CH2O exposure. Across the spectrum of Asian cities and transportation modes, exposure concentrations exhibited considerable variability, a phenomenon attributed to a multitude of factors. Primary sources of exposure encompass motor vehicle emissions, traffic dynamics, road dust, and open bus doors. Furthermore, our findings illuminate the influence of external environments, particularly in proximity to train stations, on pollutant levels inside trains. Crucial factors affecting exposure encompass ventilation conditions, travel-specific variables, seat locations, vehicle types, and meteorological influences. The culmination of this rigorous review underscores the need for standardized measurements, enhanced ventilation systems, air filtration mechanisms, the adoption of clean energy sources, and comprehensive public education initiatives aimed at reducing pollutant exposure within city transportation microenvironments. Importantly, our study contributes to the growing body of knowledge surrounding this subject, offering valuable insights for policymakers and researchers dedicated to advancing air quality standards and safeguarding public health.

Reduced commuter exposure to PM2.5 and PAHs in response to improved emission standards in bus rapid transit systems in Mexico

We evaluated impacts of progressive technological updates to bus rapid transit (BRT) systems on in-cabin concentrations of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5), and the various polyaromatic hydrocarbons (PAHs) to which commuters were exposed. PM2.5 samples were collected and real-time concentrations measured from October 2017 to March 2020 inside cabins of BRT buses equipped with Euro IV, V and VI diesel emission standards in the Mexico City Metropolitan Area (MCMA). For effective comparison, similar samplings and measurements were carried out on trains in the MCMA underground (MCU) system. Peak in-cabin PM2.5 concentrations decreased significantly (p < 0.05) by 35% from Euro IV to Euro V buses, and by 80% from Euro IV to Euro VI buses. PM2.5 concentrations inside Euro VI buses were significantly lower (p < 0.05) than in Euro IV and Euro V buses and in underground trains. The in-cabin excess (ICE) of PM2.5 relative to ambient concentrations was significantly (p < 0.05) higher for Euro IV than for Euro V buses during morning the traffic peak, and consistently higher than for Euro VI buses. Indeed, ICEs calculated for Euro VI buses were always lower than those for electricity-powered underground trains. The frequency of hotspots decreased from Euro IV to Euro VI buses due to the combined effect of low emissions and closed, air-conditioned cabins. Concentrations of total PAHs including carcinogenic species also decreased from Euro IV to Euro V buses and were below limits of detection aboard Euro VI buses. This work shows that in real-life conditions, advanced diesel technologies and cabin design significantly reduce commuters' exposure to PM2.5 and to toxic PAH compounds.

Specific size distributions of elemental and organic carbon, and polycyclic aromatic hydrocarbons emitted from petrol engine using two fuel grades

The most common commercial oils in the Chinese market are two petrol types with octane levels of 93 and 97. To determine the source spectrum of air pollutant emissions, we herein investigated the specific emission sizes of the total suspended particles (TSP), total carbon (TC), elemental carbon (EC), organic carbon (OC), and polycyclic aromatic hydrocarbons (PAHs) from a petrol engine fueled with 93# and 97# petrol in 2016 (based on Chinese national IV gasoline standard). We found that while 93# emitted a higher TSP content, 97# emitted greater TC, EC, OC, and PAHs. The highest carbon contents were found in the < 0.25 µm and 0.44-1.0 µm size fractions for the 93# and 97# petrol, respectively. OC content showed a significant positive correlation with EC, and EC2 (at 740 ℃) was the main carbon fragment in both petrol exhausts. The highest PAH content occurred in the 0.25-0.44 µm size-bin, differing from the results for TC, EC, and OC, and medium molecular weight (4 rings) PAHs were the primary component in the emissions. These results indicate that fuel composition and octane sensitivity have a prominent effect on the size distributions of TC (including EC, OC, and PAHs). Thus, more studies on the carbon content at specific emission sizes in petrol exhaust should be conducted to clarify the main factors impacting these variations.

Effect of sampling duration on the estimate of pollutant concentration behind a heavy-duty vehicle: A large-eddy simulation

Plume chasing is cost-effective, measuring individual, on-road vehicular emissions. Whereas, wake-flow-generated turbulence results in intermittent, rapid pollutant dilution and substantial fluctuating concentrations right behind the vehicle being chased. The sampling duration is therefore one of the important factors for acquiring representative (average) concentrations, which, however, has been seldom addressed. This paper, which is based on the detailed spatio-temporal dispersion data after a heavy-duty truck calculated by large-eddy simulation (LES), examines how sampling duration affects the uncertainty of the measured concentrations in plume chasing. The tailpipe dispersion is largely driven by the jet-like flows through the vehicle underbody with approximate Gaussian concentration distribution for x ≤ 0.6h, where x is the distance after the vehicle and h the characteristic vehicle size. Thereafter for x ≥ 0.6h, the major recirculation plays an important role in near-wake pollutant transport whose concentrations are highly fluctuating and positively shewed. Plume chasing for a longer sampling duration is more favourable but is logistically impractical in busy traffic. Sampling duration, also known as averaging time in the statistical analysis, thus has a crucial role in sampling accuracy. With a longer sampling (averaging) duration, the sample mean concentration converges to the population mean, improving the sample reliability. However, this effect is less pronounced in long sampling duration. The sampling accuracy is also influenced by the locations of sampling points. For the region x > 0.6h, the sampling accuracy is degraded to a large extent. As a result, acceptable sample mean is hardly achievable. Finally, frequency analysis unveils the mechanism leading to the variance in concentration measurements which is attributed to sampling duration. Those data with frequency higher than the sampling frequency are filtered out by moving average in the statistical analyses.

Sixteen-Year Monitoring of Particulate Matter Exposure in the Parisian Subway: Data Inventory and Compilation in a Database

The regularly reported associations between particulate matter (PM) exposure, and morbidity and mortality due to respiratory, cardiovascular, cancer, and metabolic diseases have led to the reduction in recommended outdoor PM10 and PM2.5 exposure limits. However, indoor PM10 and PM2.5 concentrations in subway systems in many cities are often higher than outdoor concentrations. The effects of these exposures on subway workers and passengers are not well known, mainly because of the challenges in exposure assessment and the lack of longitudinal studies combining comprehensive exposure and health surveillance. To fulfill this gap, we made an inventory of the PM measurement campaigns conducted in the Parisian subway since 2004. We identified 5856 PM2.5 and 18,148 PM10 results from both personal and stationary air sample measurements that we centralized in a database along with contextual information of each measurement. This database has extensive coverage of the subway network and will enable descriptive and analytical studies of indoor PM exposure in the Parisian subway and its potential effects on human health.

Spatiotemporal estimation of the PM2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China

PM_2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM_2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM_2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM_2.5 were explored. The LTX model performed well with R² = 0.88, RMSE of 8.73 μg/m³ and MAE of 5.85 μg/m³. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM_2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM_2.5 values, which exhibit similar distribution trends to those of the observed PM_2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM_2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.

The nexus between in-car aerosol concentrations, ventilation and the risk of respiratory infection

We examined the trade-offs between in-car aerosol concentrations, ventilation and respiratory infection transmission under three ventilation settings: windows open (WO); windows closed with air-conditioning on ambient air mode (WC-AA); and windows closed with air-conditioning on recirculation (WC-RC). Forty-five runs, covering a total of 324 km distance on a 7.2-km looped route, were carried out three times a day (morning, afternoon, evening) to monitor aerosols (PM_2.5; particulate matter < 2.5 μm and PNC; particle number concentration), CO₂ and environmental conditions (temperature and relative humidity). Ideally, higher ventilation rates would give lower in-car pollutant concentrations due to dilution from outdoor air. However, in-car aerosol concentrations increased with ventilation (WO > WC-AA > WC-RC) due to the ingress of polluted outdoor air on urban routes. A clear trade-off, therefore, exists for the in-car air quality (icAQ) versus ventilation; for example, WC-RC showed the least aerosol concentrations (i.e. four-times lower compared with WO), but corresponded to elevated CO₂ levels (i.e. five-times higher compared with WO) in 20 mins. We considered COVID-19 as an example of respiratory infection transmission. The probability of its transmission from an infected occupant in a five-seater car was estimated during different quanta generation rates (2-60.5 quanta hr^-1) using the Wells-Riley model. In WO, the probability with 50%-efficient and without facemasks under normal speaking (9.4 quanta hr^-1) varied only by upto 0.5%. It increased by 2-fold in WC-AA (<1.1%) and 10-fold in WC-RC (<5.2%) during a 20 mins trip. Therefore, a wise selection of ventilation settings is needed to balance in-car exposure in urban areas affected by outdoor air pollution and that by COVID-19 transmission. We also successfully developed and assessed the feasibility of using sensor units in static and dynamic environments to monitor icAQ and potentially infer COVID-19 transmission. Further research is required to develop automatic-alarm systems to help reduce both pollutant exposure and infection from respiratory COVID-19 transmission.

Fine particulate matter exposure in four transport modes of Greater Cairo

The number of daily commuters in Greater Cairo has exceeded 15 million nevertheless personal exposure studies in transport microenvironments are limited. The aim of this study is to quantify PM_2.5 exposure during peak hours in four transport modes of Greater Cairo - car (windows-open, windows-closed with recirculation and AC-on), microbus (windows-open), cycling and walking - and understand its underlying drivers. Data was collected using a pDR-1500 monitor and analysed to capture concentration variations, spatial variability, exposure doses, commuting costs versus inhaled doses, health burden and economic losses. Car with recirculation resulted in the least average PM_2.5 concentrations (32 ± 6 μg/m³), followed by walking (77 ± 35 μg/m³), car with windows-open (82 ± 32 μg/m³), microbus with windows-open (96 ± 29 μg/m³) and cycling (100 ± 28 μg/m³). Evening hours observed average PM_2.5 concentrations by 26-58% lesser than morning. Spatial variability analysis showed that 75th-90th percentile PM_2.5 concentrations coincided with congested spots. Cycling and walking lanes are rare hence commuters are exposed to surges in PM_2.5 concentrations when passing near construction and solid waste burning sites. Cycling and walking also resulted in inhaling 40-times and 32-times higher PM_2.5 dose per kilometre than for car with recirculation. Commuting by microbus cost (with windows-open) ~45% of car cost (with recirculation) but it resulted in 4-times higher inhaled PM_2.5 dose. As expected due to the lowest PM_2.5 exposure concentrations, health burden resulting from car travel (with recirculation) caused the least death rates of 0.07 (95% CI 0.07-0.08) prematures deaths per 100,000 commuters/year while microbus with windows-open resulted in the highest death rates; 0.52 (95% CI 0.49-0.56). Microbus deaths represent 57% of national economic losses due to PM_2.5 exposure amongst the four transport modes. This study provides real-time exposure data and analyses its implications on commuter health as a first step in informed decision-making and better urban planning.

Commuter types identified using clustering and their associations with source-specific PM2.5

Traffic-related fine particulate matter air pollution (tr-PM_2.5) has been associated with adverse health outcomes such as cardiopulmonary morbidity and mortality, with in-vehicle tr-PM_2.5 exposure contributing to total personal pollution exposure. Trip characteristics, including time of day, day of the week, and traffic congestion, are associated with in-vehicle PM_2.5 exposures. We hypothesized that some commuter characteristics, such as whether commuters travel primarily during rush hour, would also be associated with increased tr-PM_2.5 exposures. The commute data consisted of unscripted personal vehicle trips of 46 commuters in the Washington, D.C. metro area over 48-h, with a total of 320 trips. We identified commuter types using sparse K-means clustering, which identifies the hours throughout the day important for clustering commuters. Source-specific PM_2.5 over 48 h was estimated using Positive Matrix Factorization. Linear regression was used to estimate differences in source-specific PM_2.5 by commuter cluster. Two commuter clusters were identified using the clustering approach: rush hour commuters, who primarily travelled during rush hour, and sporadic commuters, who travelled throughout the day. The hours given the largest weights by sparse K-means were 7-8 a.m. and 6-7 p.m., corresponding to peak travel times. Integrated black carbon (BC) was higher for rush hour commuters (median = 3.1 μg/m³ (IQR = 1.5)) compared to sporadic commuters (2.0 μg/m³ (IQR = 1.9)). Mobile PM_2.5, consisting primarily of tailpipe emissions and brake/tire wear, was also higher for rush hour commuters (2.9 μg/m³ (IQR = 1.6)) compared to sporadic commuters (2.1 μg/m³ (IQR = 2.4)), though this difference was not statistically significant in regression models. Estimated differences between commuter types for secondary/mixed PM_2.5 and road salt PM_2.5 were smaller. Further research may elucidate whether commuter characteristics are an efficient way to identify individuals with highest tr-PM_2.5 exposures associated with commuting and to develop effective mitigation strategies.

Particulate Matter Exposures under Five Different Transportation Modes during Spring Festival Travel Rush in China

Serious traffic-related pollution and high population density during the spring festival (Chinese new year) travel rush (SFTR) increases the travelers’ exposure risk to pollutants and biohazards. This study investigates personal exposure to particulate matter (PM) mass concentration when commuting in five transportation modes during and after the 2020 SFTR: China railway high-speed train (CRH train), subway, bus, car, and walking. The routes are selected between Nanjing and Xuzhou, two major transportation hubs in the Yangtze Delta. The results indicate that personal exposure levels to PM on the CRH train are the lowest and relatively stable, and so it is recommended to take the CRH train back home during the SFTR to reduce the personal PM exposure. The exposure level to PM2.5 during SFTR is twice as high as the average level of Asia, and it is higher than the WHO air quality guideline (AQG).

Difference in ambient-personal exposure to PM2.5 and its inflammatory effect in local residents in urban and peri-urban Beijing, China: results of the AIRLESS project

Measurement of ambient fine particulate matter (PM_2.5) is often used as a proxy of personal exposure in epidemiological studies. However, the difference between personal and ambient exposure, and whether it biases the estimates of health effects remain unknown. Based on an epidemiological study (AIRLESS) and simultaneously launched intensive monitoring campaigns (APHH), we quantified and compared the personal and ambient exposure to PM_2.5 and the related health impact among residents in Beijing, China. In total, 123 urban and 128 peri-urban non-smoking participants were recruited from two well-established cohorts in Beijing. During winter 2016 and summer 2017, each participant was instructed to carry a validated personal air monitor (PAM) to measure PM_2.5 concentration at high spatiotemporal resolution for seven consecutive days in each season. Multiple inflammatory biomarkers were measured, including exhaled NO, blood monocytes counts and C-reactive protein. Linear mixed-effect models were used for the associations between exposure and health outcomes with adjustment for confounders. The average level of daily personal exposure to PM_2.5 was consistently lower than using corresponding ambient concentration, and the difference is greater during the winter. The personal to ambient (P/A) ratio of exposure to PM_2.5 exhibited an exponentially declining trend, and showed larger variations when ambient PM_2.5 levels < 25 μg m^-3. Personal exposure to PM_2.5 was significantly associated with the increase in respiratory and systemic inflammatory biomarkers; however, the associations were weaker or became insignificant when ambient concentrations were used. Exposure to ambient PM_2.5 might not be a good proxy to estimate the health effect of exposure to personal PM_2.5.

A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods

People typically spend most of their time indoors. It is of importance to establish prediction models to estimate PM_2.5 concentration in indoor environments (e.g., residential households) to allow accurate assessments of exposure in epidemiological studies. This study aimed to develop models to predict PM_2.5 concentration in residential households. PM_2.5 concentration and related parameters (e.g., basic information about the households and ventilation settings) were collected in 116 households during the winter and summer seasons in Hong Kong. Outdoor PM_2.5 concentration at households was estimated using a land-use regression model. The random forest machine learning algorithm was then applied to develop indoor PM_2.5 prediction models. The results show that the random forest model achieved a promising predictive accuracy, with R² and cross-validation R² values of 0.93 and 0.65, respectively. Outdoor PM_2.5 concentration was the most important predictor variable, followed in descending order by the household marked number, outdoor temperature, outdoor relative humidity, average household area and air conditioning. The external validation result using an independent dataset confirmed the potential application of the random forest model, with an R² value of 0.47. Overall, this study shows the value of a combined land-use regression and machine learning approach in establishing indoor PM_2.5 prediction models that provide a relatively accurate assessment of exposure for use in epidemiological studies.

In-car particulate matter exposure across ten global cities

Cars are a commuting lifeline worldwide, despite contributing significantly to air pollution. This is the first global assessment on air pollution exposure in cars across ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Portable laser particle counters were used to develop a proxy of car-user exposure profiles and analyse the factors affecting particulate matter ≤2.5 μm (PM_2.5; fine fraction) and ≤10 μm (PM_2.5-10; coarse fraction). Measurements were carried out during morning, off- and evening-peak hours under windows-open and windows-closed (fan-on and recirculation) conditions on predefined routes. For all cities, PM_2.5 and PM₁₀ concentrations were highest during windows-open, followed by fan-on and recirculation. Compared with recirculation, PM_2.5 and PM₁₀ were higher by up to 589% (Blantyre) and 1020% (São Paulo), during windows-open and higher by up to 385% (São Paulo) and 390% (São Paulo) during fan-on, respectively. Coarse particles dominated the PM fraction during windows-open while fine particles dominated during fan-on and recirculation, indicating filter effectiveness in removing coarse particles and a need for filters that limit the ingress of fine particles. Spatial variation analysis during windows-open showed that pollution hotspots make up to a third of the total route-length. PM_2.5 exposure for windows-open during off-peak hours was 91% and 40% less than morning and evening peak hours, respectively. Across cities, determinants of relatively high personal exposure doses included lower car speeds, temporally longer journeys, and higher in-car concentrations. It was also concluded that car-users in the least affluent cities experienced disproportionately higher in-car PM_2.5 exposures. Cities were classified into three groups according to low, intermediate and high levels of PM exposure to car commuters, allowing to draw similarities and highlight best practices.

Characteristics and toxicological effects of commuter exposure to black carbon and metal components of fine particles (PM2.5) in Hong Kong

Epidemiological studies have demonstrated significant associations between traffic-related air pollution and adverse health outcomes. Personal exposure to fine particles (PM_2.5) in transport microenvironments and their toxicological properties remain to be investigated. Commuter exposures were investigated in public transport systems (including the buses and Mass Transit Railway (MTR)) along two sampling routes in Hong Kong. Real-time sampling for PM_2.5 and black carbon (BC), along with integrated PM_2.5 sampling, were performed during the warm and cold season of 2016-2017, respectively. Commuter exposure to BC during 3-hour commuting time exhibited a wider range, from 3.4 to 4.6 μg/m³ on the bus and 5.5 to 8.7 μg/m³ in MTR cabin (p < .05). PM_2.5 mass and major chemical constituents (including organic carbon (OC), elemental carbon (EC), and metals) were analyzed. Cytotoxicity, including cellular reactive oxygen species (ROS) production, was determined in addition to acellular ROS generation. PM_2.5 treatment promoted the ROS generation in a concentration-dependent manner. Consistent diurnal variations were observed for commuter exposure to BC and PM_2.5 components, along with cellular and acellular ROS generation, which marked with two peaks during the morning (08:00-11:00) and evening rush hours (17:30-20:30). Commuter exposures in the MTR system were characterized by higher levels of PM_2.5 and elemental components (e.g., Ca, Cr, Fe, Zn, Ba) compared to riding the bus, along with higher cellular and acellular ROS production (p < .01). These metals were attributed to different sources: rail tracks, wheels, brakes, and crustal origin. Weak to moderate associations were shown for the analyzed transition metals with PM_2.5-induced cell viability and cellular ROS. Multiple linear regression analysis revealed that Ni, Zn, Mn, Fe, Ti, and Co attributed to cytotoxicity and ROS generation. These findings underscore the importance of commuter exposures and their toxic effects, urging effective mitigating strategies to protect human health.

Dispersion behaviors of exhaust gases and nanoparticle of a passenger vehicle under simulated traffic light driving pattern

In the present study, the flow structure and pollutants dispersions were investigated by experiment and simulation on a typical passenger vehicle under simulated traffic light driving pattern. Some important findings were achieved: 1) gaseous pollutants diffuse drastically during first 0.3-0.6 m distance depending on wind velocity, at 1.25 m/s wind speed which is the similar level of exhaust gas, the pollutant concentration rises suddenly at ~0.6 m because exhaust plume is twisted by bottom gas flow, and a low velocity zone is produced; 2) as wind speed increases, the vehicle-induced turbulence is more and more important on pollutant dispersion pattern than exhaust plume dynamics. For instance, at 1.25 m/s and 4.17 m/s wind speeds, pollutants decrease to zero at ~1.6 m behind tail pipe, but at 0 m/s condition, pollutant relative fraction is still at ~0.12 level even at very long distance; 3) solid particle has larger attenuation rate than gaseous pollutants, only after ~0.6 m the particle number (PN) and diameter are very close to background values. Solid particle can diffuse to farther distance in vehicle transverse direction, when a car passes through the pedestrians with a 3 m distance, pedestrians expose to 2.6-3 times higher PN relative to atmosphere with diameters of 28-33 nm, this is very hazardous for human health; 4) exhaust pollutants disperse difficultly when followed by a car with a commonly waiting distance. At free dispersion scenario only behind ~0.6 m, PN decreases to 5800 #/cm³ (background value), but in-cabin PN of the following car (behind 0.8 m) rises to 3.5 × 10⁴ #/cm³ (even after 2-3 times decay through ventilation system). This study provides implications for future studies on transport planning.

Chemical characterization of PM2.5 emissions and atmospheric metallic element concentrations in PM2.5 emitted from mobile source gasoline-fueled vehicles

Fine particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5), particularly from the in-use gasoline-fueled vehicles, is a leading air quality pollutant and the chemical composition of PM_2.5 is vital to the practical issues of climate change, health effects, and pollution control policies, inter alia. These atmospheric fine particulate matters (PM_2.5) emitted from the exhausts of mobile source gasoline-fueled vehicles constitute substantial risks to human health through inhalation, and most importantly, affect urban air quality. Therefore, in order to explicitly determine the inhalation risks of PM_2.5 which could potentially contain a significant amount of chemicals and metallic elements (MEs) concentration, we investigated the chemical composition (comprising of carbonaceous species and metallic elements) of PM_2.5 emissions from mobile source gasoline-fueled vehicles. To further examine the chemical composition and metallic elements concentration in PM_2.5 from the exhausts of mobile source gasoline-fueled vehicles, we systematically investigated PM_2.5 emission samples collected from the exhausts of fifteen (15) mobile source gasoline-fueled vehicles. Our study has equally also determined the chemical compositions based on carbonaceous species (organic carbon - OC and elemental carbon - EC). Furthermore, the concentrations of PM_2.5 and metallic elements (Ca, Al, Zn, K, Ca, Fe, Mg and Cr) in PM_2.5 were analyzed with the help of Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The details of the tested gasoline-fueled vehicles cover the model years, consisting of the vehicles registered from 2000 to 2017 from several vehicle manufacturers (or brands) with various running mileages ranging from 123.4 to 575,844 km (average 123,105 km). Our results established that elemental carbon (EC) and organic carbon (OC) were the most significant concentrations of carbonaceous species. The concentration of metallic elements in PM_2.5 and chemical characterization were studied by their relationship with atmospheric PM_2.5 and the results showed that the metallic elements concentration in PM_2.5 were in descending order as follows: Ca > Al > Zn > K > Fe > Mg > Cr. These results will help us to further understand how PM_2.5 emissions from the exhausts of in-use gasoline-fueled vehicles contribute to both chemical and atmospheric metallic elements concentration in the ambient air.

Determinants of personal exposure to fine particulate matter in the retired adults - Results of a panel study in two megacities, China

This study aimed to investigate the relationship between outdoor, indoor, and personal PM_2.5 exposure in the retired adults and explore the effects of potential determinants in two Chinese megacities. A longitudinal panel study was conducted in Nanjing (NJ) and Beijing (BJ), China, and thirty-three retired non-smoking adults aged 43-86 years were recruited in each city. Repeated measurements of outdoor-indoor-personal PM_2.5 concentrations were measured for five consecutive 24-h periods during both heating and non-heating seasons using real-time and gravimetric methods. Time-activity and household characteristics were recorded. Mixed-effects models were applied to analyze the determinants of personal PM_2.5 exposure. In total, 558 complete sets of collocated 24-h outdoor-indoor-personal PM_2.5 concentrations were collected. The median 24-h personal PM_2.5 exposure concentrations ranged from 43 to 79 μg/m³ across cities and seasons, which were significantly greater than their corresponding indoor levels (ranging from 36 to 68 μg/m³, p < 0.001), but significantly lower than outdoor levels (ranging from 43 to 95 μg/m³, p < 0.001). Indoor and outdoor PM_2.5 concentrations were the strongest determinants of personal exposures in both cities and seasons, with R_M² ranging from 0.814 to 0.915 for indoor and from 0.698 to 0.844 for outdoor PM_2.5 concentrations, respectively. The personal-outdoor regression slopes varied widely among seasons, with a pronounced effect in BJ (NHS: 0.618 ± 0.042; HS: 0.834 ± 0.023). Ventilation status, indoor PM_2.5 sources, personal characteristics, and meteorological factors, were also found to influence personal exposure levels. The city and season-specific models developed here are able to account for 89%-93% of the variance in personal PM_2.5 exposure. A LOOCV analysis showed an R² (RMSE) of 0.80-0.90 (0.21-0.36), while a 10-fold CV analysis demonstrated a R² (RMSE) of 0.83-0.90 (0.20-0.35). By incorporating potentially significant determinants of personal exposure, this modeling approach can improve the accuracy of personal PM_2.5 exposure assessment in epidemiologic studies.

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

While the measurement of particulate matter (PM) with a diameter of less than 2.5 μm (PM_2.5) has been conducted for personal exposure assessment, it remains unclear how models that integrate microenvironmental levels with resolved activity and location information predict personal exposure to PM. We comprehensively investigated PM_2.5 concentrations in various microenvironments and estimated personal exposure stratified by the microenvironment. A variety of microenvironments (>200 places and locations, divided into 23 components according to indoor, outdoor, and transit modes) in a community were selected to characterize PM_2.5 concentrations. Infiltration factors calculated from microenvironmental/central-site station (M/S) monitoring campaigns with time-activity patterns were used to estimate time-weighted exposure to PM_2.5 for university students. We evaluated exposures using a four-stage modeling approach and quantified the performance of each component. It was found that the SidePak monitor overestimated the concentration by 3.5 times as compared with the filter-based measurements. Higher mean concentrations of PM_2.5 were observed in the Taoist temple and night market microenvironments; in contrast, lower concentrations were observed in air-conditioned offices and car microenvironments. While the exposure model incorporating detailed time-location information and infiltration factors achieved the highest prediction (R² = 0.49) of personal exposure to PM_2.5, the use of indoor, outdoor, and transit components for modeling also generated a consistent result (R² = 0.44).

Low-cost sensors as an alternative for long-term air quality monitoring

Low-cost air quality sensors are increasingly being used in many applications; however, many of their performance characteristics have not been adequately investigated. This study was conducted over a period of 13 months using low-cost air quality monitors, each comprising two low-cost sensors, which were subjected to a wide range of pollution sources and concentrations, relative humidity and temperature at four locations in Australia and China. The aim of the study was to establish the performance characteristics of the two low-cost sensors (a Plantower PMS1003 for PM_2.5 and an Alphasense CO-B4 for carbon monoxide, CO) and the KOALA monitor as a whole under various conditions. Parameters evaluated included the inter-variability between individual monitors, the accuracy of monitors in comparison with the reference instruments, the effect of temperature and RH on the performance of the monitors, the responses of the PM_2.5 sensors to different types of aerosols, and the long-term stability of the PM_2.5 and CO sensors. The monitors showed high inter-correlations (r > 0.91) for both PM_2.5 and CO measurements. The monitor performance varied with location, with moderate to good correlations with reference instruments for PM_2.5 (0.44< R² < 0.91) and CO (0.37< R² < 0.90). The monitors performed well at relative humidity < 75% and high temperature conditions; however, two monitors in Beijing failed at low temperatures, probably due to electronic board failure. The PM_2.5 sensor was less sensitive to marine aerosols and fresh vehicle emissions than to mixed urban background emissions, aged traffic emissions and industrial emissions. The long-term stability of the PM_2.5 and CO sensors was good, while CO relative errors were affected by both high and low temperatures. Overall, the KOALA monitors performed well in the environments in which they were operated and provided a valuable contribution to long-term air quality monitoring within the elucidated limitations.

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.

STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains

Human exposure to particulate matter and other environmental species is difficult to estimate in large populations. Individuals can encounter significant and acute variations in exposure over small spatiotemporal scales. Exposure is strongly tied to both the environmental and activity contexts that individuals experience. Here we present the development of an agent-based model to simulate human exposure at high spatiotemporal resolutions. The model is based on simulated activity and location trajectories on a per-person basis for large geographical areas. We demonstrate that the model can successfully estimate trajectories and that activity patterns have been validated against traffic patterns and that can be integrated with exposure-agent geographical distributions to estimate total human exposure.

Particle exposure and inhaled dose while commuting in Lisbon

While commuting, individuals are exposed to high concentrations of urban air pollutants that can lead to adverse health effects. This study aims to assess commuters' exposure to particulate matter (PM) when travelling by car, bicycle, metro and bus in Lisbon. Mass concentrations of PM_2.5 and PM₁₀ were higher in the metro. On the other hand, the highest BC and PN_0.01-1 average concentrations were found in car and bus mode, respectively. In cars, the outdoor concentrations and the type of ventilation appeared to affect the indoor concentrations. In fact, the use of ventilation led to a decrease of PM_2.5 and PM₁₀ concentrations and to an increase of BC concentrations. The highest inhaled doses were mostly observed in bicycle journeys, due to the longest travel periods combined with enhanced physical activity and, consequently, highest inhalation rates.

The TRAX Light-Rail Train Air Quality Observation Project

Observing air quality from sensors onboard light rail cars in Salt Lake County, Utah began as a pilot study in 2014 and has now evolved into a five-year, state-funded program. This metropolitan region suffers from both elevated ozone levels during summer and high PM2.5 events during winter. Pollution episodes result predominantly from local anthropogenic emissions but are also impacted by regional transport of dust, chemical precursors to ozone, and wildfire smoke, as well as being exacerbated by the topographical features surrounding the city. Two electric light-rail train cars from the Utah Transit Authority light-rail Transit Express (“TRAX”) system were outfitted with PM2.5 and ozone sensors to measure air quality at high spatial and temporal resolutions in this region. Pollutant concentration data underwent quality control procedures to determine whether the train motion affected the readings and how the sensors compared against regulatory sensors. Quality assurance results from data obtained over the past year show that TRAX Observation Project sensors are reliable, which corroborates earlier preliminary validation work. Three case studies from summer 2019 are presented to illustrate the strength of the finely-resolved air quality observations: (1) an elevated ozone event, (2) elevated particulate pollution resulting from 4th of July fireworks, and (3) elevated particle pollution during a winter time inversion event. The mobile observations were able to capture spatial gradients, as well as pollutant hotspots, during both of these episodes. Sensors have been recently added to a third light rail train car, which travels on a north–south oriented rail line, where air quality was unable to be monitored previously. The TRAX Observation Project is currently being used to provide reliable pollutant data for health studies and inform urban planning efforts. Links to real-time data displays and updated information on the quality-controlled data from this study are available on the webpage for the Department of Atmospheric Sciences at the University of Utah.

A feasible experimental framework for field calibration of portable light-scattering aerosol monitors: Case of TSI DustTrak

Portable light-scattering aerosol monitors (PLSAMs) can supplement existing air quality monitoring networks through measuring air pollutant exposure concentrations at high spatiotemporal resolution. However, data collected by PLSAMs are often subject to the simplicity of measurement principle which may lead to errors compared to the regulatory data observed at fixed-site air quality monitoring stations. The main objective of this study was to develop a feasible experimental framework to assess the influence of key factors (e.g., relative humidity (RH)) on the performance of PLSAMs in the real-world conditions. Following the proposed framework, the accuracy and precision of the TSI DustTrak aerosol monitor were evaluated through side-by-side comparison with the stationary reference instruments (SRIs) while taking characteristics of particles, RH, and the concentration range into consideration. DustTrak generally demonstrated low accuracy but high precision in measuring PM_2.5 concentrations at the two selected stations. Three calibration models between DustTrak and the SRIs were used to bias correct the DustTrak PM_2.5 measurements. The RH-adjusted linear regression calibration method led to better calibration results than the simple linear regression method and the RH-adjusted empirical method, with CV R² values higher than 0.97, root mean square error less than 1.0 μg/m³, and accuracy values at 3% for two DustTraks. The proposed experimental framework can be extended to field calibration of various types of PLSAMs, and the obtained calibration results can promote a more accurate investigation of particle air pollution using these PLSAMs.

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

Air Pollution Monitoring for Health Research and Patient Care. An Official American Thoracic Society Workshop Report

Air quality data from satellites and low-cost sensor systems, together with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much-needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network. To address these issues, the American Thoracic Society, the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, and the National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: 1) current and near-term capabilities in air pollution monitoring, 2) data assimilation from multiple technology platforms, 3) critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network, and 4) risk communication and health messaging. Recommendations for research and improved use were identified during the workshop, including a recognition that the integration of data across monitoring technology groups is critical to maximizing the effectiveness (e.g., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together, these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible, and reliable air pollution data and forecasts to health professionals, as well as to all global citizens.

In-vehicle PM2.5 personal concentrations in winter during long distance road travel in India

During travel, passengers are exposed to high concentrations of PM which constitute a significant fraction of daily personal exposures. We carried out comprehensive mobile monitoring for a distance of 400km on an Indian National Highway during the winter season to evaluate the PM_2.5 Personal Concentrations (PC) and mass exposure in three traffic microenvironments (public bus, car with AC (Car CW) and car without AC (Car OW)) and to quantify the key factors that influence it. The mean concentrations were highest inside Car OW (175.3±142.7μgm^-3) followed by bus (134.0±113.9μgm^-3) and lowest in Car CW (78.8±37.1μgm^-3). PC during in-city highway sections were greater than out-city highway sections during Bus and Car OW journeys. PC were higher during morning than evening journeys in Bus and Car OW. Mean PC in different seating positions in Bus followed the trend: middle>rear>front. Results of the Linear Mixed-Effects Models (LMM) indicated that journey timings were the significant predictors of PC for Bus and Car OW. The exposures per unit time followed trend: Car OW>Bus>Car CW. Total mass of inhaled exposures however followed a different trend: Bus>Car OW>Car CW, because Bus needed longer duration to cover the entire distance. Car CW users experienced both the least PC and mass exposures. We estimated that the road repairing works contributed ~22% in Bus and Car OW, and ~12% in Car CW increment in mass exposures. These findings indicate that management of exposures needs to consider mass exposures in addition to PC, for curtailing the adverse health effects relating to long distance journeys. Highway authorities should focus on early completion of construction and repairing activities to reduce exposures to passengers.

Characterization and Risk Assessment of Particulate Matter and Volatile Organic Compounds in Metro Carriage in Shanghai, China

Air quality in transportation microenvironment has received widespread attention. In this study, the exposure levels of volatile organic compounds (VOCs) and particulate matter that have a diameter of less than 2.5 micrometers (PM2.5) in Shanghai metro system were measured simultaneously, and their risks to human health under different driving conditions were then assessed. The results showed that VOCs, PM2.5 concentrations and life cancer risk (LCR) of four VOCs (benzene, formaldehyde, ethylbenzene, and acetaldehyde) in the old metro carriages were about 3 times, 3 times and 2 times higher than those in the new metro carriages, respectively. This difference can be ascribed to the fact that air filtration system in the new metro trains is significantly improved. The VOC levels, PM2.5 concentrations and LCR of VOCs on the above-ground track were slightly higher than those on the underground track. This is due to less outdoor polluted air entering into the carriage on the underground track. Number of passengers also had an effect on VOCs and PM2.5 concentrations in metro carriages. Additionally, the LCR of VOCs inside metro trains should not be ignored (7.69 × 10−6~1.47 × 10−5), especially inside old metro trains with the old ventilation system.

Cyclists’ Multiple Environmental Urban Exposures—Comparing Subjective and Objective Measurements

Citizens in urban areas are exposed to multiple environmental stressors like noise, heat, and air pollution, with impact on human health. There is a great deal of evidence that connects human health, objective environmental exposure, and place of residence. However, little is known about subjective and objective multiple personal exposures while being mobile. To address this research gap, this paper presents results from a mixed-methods exploratory study with cyclists in the City of Leipzig, Germany. In the summer of 2017, cyclists (n = 66) wore a unique combination of sensors that measured particle number counts (PNC), noise, humidity, temperature, geolocation, and the subjective perception of each exposure on everyday routes for one week (n = 730). A smartphone application was developed to question participants about their perception of subjective exposure. The data were analyzed with three aims: (i) to compare the multiple exposure profiles of the cyclists, (ii) to contrast the objective data and subjective individual perception, and (iii) to examine the role of route decision-making and awareness of health impacts for healthier route choices. The results indicate distinct differences between the exposure profiles of cyclists. Over 80% of the cyclists underestimated their exposure to noise and air pollution. Except for heat, no significant associations between the objective and subjective data were found. This reveals an exposure awareness gap that needs to be considered in urban health planning and risk communication. It is argued that knowledge about health impacts and route characteristics plays a crucial role in decision-making about route choices. The paper concludes with suggestions to harness smart sensing for exposure mitigation and research in health geography.

Chemical characterization and source apportionment of PM2.5 personal exposure of two cohorts living in urban and suburban Beijing

In the study, personal PM_2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM_2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM_2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM_2.5 exposures and produced poor correlations between personal and ambient PM_2.5. These results demonstrate that individual behavior strongly affects personal PM_2.5 exposure. Six primary sources of personal PM_2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM_2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.

Characterization of Urban Subway Microenvironment Exposure- A Case of Nanjing in China

Environmental quality in public rail transit has recently raised great concern, with more attention paid to underground subway microenvironment. This research aimed to provide guidance for healthy urban subway microenvironments (sub-MEs) according to comprehensive micro-environmental categories, including thermal environment, air quality, lighting environment, and acoustic environment from both practical and regulation perspectives. Field sampling experiments were conducted in Nanjing Metro Line X (NMLX). Descriptive analysis, correlation analysis and one-way analysis of variance were used to investigate the status quo of urban sub-MEs. A paired samples t-test was then performed to compare among subway station halls, platforms, and in-cabin trains based on integrated sub-MEs. Results show that relative humidity, air velocity, respirable particulate matter (PM10) concentration, and illuminance dissatisfy the requirements in relevant national standards. Significant difference was observed in lighting environment between station hall and platform. It was detected platforms are warmer and more polluted than train cabins. Additionally, subway trains generate main noise on platform which is much louder when leaving than arriving. Protective strategies for sub-ME improvement as well as principles for updating standards were proposed from a proactive point of view. The findings are beneficial for moving towards healthy urban sub-MEs and more sustainable operation of subway systems.

Assessing Effect of Targeting Reduction of PM2.5 Concentration on Human Exposure and Health Burden in Hong Kong Using Satellite Observation

Targeting reduction of PM2.5 concentration lessens population exposure level and health burden more effectively than uniform reduction does. Quantitative assessment of effect of the targeting reduction is limited because of the lack of spatially explicit PM2.5 data. This study aimed to investigate extent of exposure and health benefits resulting from the targeting reduction of PM2.5 concentration. We took advantage of satellite observations to characterize spatial distribution of PM2.5 concentration at a resolution of 1 km. Using Hong Kong of China as the study region (804 satellite’s pixels covering its residential areas), human exposure level (cρ) and premature mortality attributable to PM2.5 (Mort) for 2015 were estimated to be 25.9 μg/m3 and 4112 people per year, respectively. We then performed 804 diagnostic tests that reduced PM2.5 concentrations by −1 μg/m3 in different areas and a reference test that uniformly spread the −1 μg/m3. We used a benefit rate from targeting reduction (BRT), which represented a ratio of declines in cρ (or Mort) with and without the targeting reduction, to quantify the extent of benefits. The diagnostic tests estimated the BRT levels for both human exposure and premature mortality to be 4.3 over Hong Kong. It indicates that the declines in human exposure and premature mortality quadrupled with a targeting reduction of PM2.5 concentration over Hong Kong. The BRT values for districts of Hong Kong could be as high as 5.6 and they were positively correlated to their spatial variabilities in population density. Our results underscore the substantial exposure and health benefits from the targeting reduction of PM2.5 concentration. To better protect public health in Hong Kong, super-regional and regional cooperation are essential. Meanwhile, local environmental policy is suggested to aim at reducing anthropogenic emissions from mobile and area (e.g., residential) sources in central and northwestern areas.

Short-term effects of particulate matter in metro cabin on heart rate variability in young healthy adults: Impacts of particle size and source

BACKGROUND

Metro system has become popular in urban areas. However, short-term effects of size-fractionated particulate matter (PM) on cardiac autonomic function in metro system remain unexplored.

OBJECTIVES

To explore the contribution of ambient PM to in-cabin PM and investigate the short-term effects of exposure to size-fractionated PM and black carbon (BC) in metro system on cardiac autonomic function in young healthy adults.

METHODS

Thirty nine young healthy adults were asked to travel in metro system during 9:00-13:00 on a weekends between March and May 2017. We performed continuous ambulatory electrocardiogram monitoring for each of them, and measured real-time size-fractionated PM, BC, nitrogen dioxide, nitric oxide, carbon dioxide, ozone, noise, temperature and relative humidity in metro cabin. We also collected the data of ambient PM_2.5 (aerodynamic diameter < 2.5 µm) concentrations in Beijing. Linear regression model was used to estimate the infiltration factor of ambient PM_2.5 to assess the relationship between metro cabin PM and ambient PM. Mixed-effects model was used to estimate the associations between changes in HRV parameters and PM_0.5 (aerodynamic diameter < 0.5 µm), PM_0.5-2.5 (aerodynamic diameter between 0.5 µm and 2.5 µm), PM_2.5-10 (aerodynamic diameter between 2.5 µm and 10 µm), and BC, respectively.

RESULTS

We found that size-fractionated PM in metro systems were significantly associated with HRV parameters. Per IQR (interquartile range) increase in PM_0.5 (1.6*10⁷/m³) in 1-h moving average concentration was associated with a 13.96% (95% CI: - 18.99%, - 8.61%) decrease in SDNN (standard deviation of normal-to-normal intervals). Similar inverse associations were found between size-fractionated PM exposure and LF (low frequency power), HF (high frequency power), respectively, and smaller particles had greater effects on HRV parameters at shorter lag time. Sex of participants modified the adverse associations between size-fractionated PM and HRV. An IQR of 1-h PM_0.5 increasing was associated with a decrease of 6.05% (95% CI: - 22.87%, - 14.44%) in males and a 34.87% (95% CI: - 49.59%, - 15.85%) in females in LF (P for interaction = 0.026). The infiltration factor of ambient PM_2.5 was 0.39 (95% CI: 0.33, 0.45). It is estimated that PM_2.5 originated from ambient air may account for 20.2% of the PM measured in metro cabin. Per IQR increase in BC (5.5 μg/m³) in 5-min, 1-h, and 2-h moving averages, a primary tracer for ambient PM from combustion source, was associated with decreases of 0.84% (95% CI: - 1.20%, - 0.47%), 2.22% (95% CI: - 3.20%, - 1.22%), and 4.44% (95% CI: - 6.28%, - 2.56%) in SDNN, respectively.

CONCLUSIONS

Short-term exposure to PM may disturb metro commuter's cardiac autonomic function, and the potential effects depend on the size of PM and the sex of commuters. Ambient PM from combustion source may have adverse effects on the cardiac autonomic function of passengers in cabin.

The value of using seasonality and meteorological variables to model intra-urban PM2.5 variation

A yearlong air monitoring campaign was conducted to assess the impact of local temperature, relative humidity, and wind speed on the temporal and spatial variability of PM_2.5 in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM_2.5 variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM_2.5 events that contributed to elevated seasonal PM_2.5 levels. Similarly, in spring, high PM_2.5 events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM_2.5 fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM_2.5 and meteorological variables either changed from positive to negative or dissolved depending on the season. Overall, the results suggest that mixed effects analysis with season and site as fixed factors and meteorological variables as covariates could increase the explanatory value of LUR models for PM_2.5.

Factors affecting variability in PM2.5 exposure concentrations in a metro system

The objectives of this study were to: (1) evaluate PM_2.5 inflow to metro train cabins when doors open at stations; (2) assess the spatial and temporal variability in PM_2.5 exposure concentration; and (3) quantify the relationship between in-cabin concentration versus outdoor and non-ambient PM_2.5. We measured in-cabin PM_2.5 concentrations using portable monitors at the door-side and center of a train cabin simultaneously on a Hong Kong metro line. In addition, platform and in-cabin pollutant concentrations near a train door were simultaneously measured. Short-term spikes in PM_2.5 concentrations typically occur near train doors when doors open, related to inflow of ambient air aboveground and tunnel air underground. In-cabin PM_2.5 exposure concentrations are typically lower away from the doors when the doors open. PM_2.5 concentrations inside train cabins and on station platform operating above-ground are more influenced, compared to underground, by outdoor PM_2.5. Moreover, non-ambient sources contribute approximately 50% of train in-cabin and station platform PM_2.5 concentrations during underground operation. The results help more accurately quantify commuting PM_2.5 exposure on a metro system, and can be used to improve population-based exposure simulation models.