Personal exposure to PM2.5 in five commuting modes under hazy and non-hazy conditions

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

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

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.

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.

Lifestyle practices that reduce seasonal PM2.5 exposure and their impact on COPD

Particulate matter (PM) is a major air pollutant that has led to global health concerns and can cause and exacerbate chronic obstructive pulmonary disease (COPD). We asked patients with COPD to complete a detailed questionnaire about their lifestyle practices to reduce PM_2.5 exposure and analyzed the relationship between ambient PM_2.5 concentrations and lifestyle practices. We prospectively enrolled 104 COPD patients from four hospitals in different areas of Korea. They completed detailed questionnaires twice (at enrollment and the end of the study) and Internet of Things-based sensors were installed in their homes to continuously measure PM_2.5 for 1 year. The relationship between PM_2.5 concentrations, lifestyle practices, and COPD exacerbations were analyzed in each season. The PM_2.5 concentration was higher outdoors than indoors in all seasons except summer, and the difference was largest in winter. The six lifestyle practices that significantly lowered the annual indoor PM_2.5 concentration compared with the outdoors. The higher the economic status and educational level of patients, the lower the indoor PM_2.5 concentration. Some lifestyle practices were associated with reduced small airway resistance, presented as R5-R20 determined by impulse oscillometry, and scores of the St. George's Respiratory Questionnaire. Some lifestyle practices are associated with reduced indoor PM_2.5 concentrations and can even affect clinical outcomes, including small airway resistance and quality of life of COPD patients.

Efficiency of portable air purification on public buses: A pilot study

High concentrations of fine particulate matter (PM_2.5) have been frequently reported in public transit systems and can cause adverse health effect. The portable air purifier is an inexpensive solution that could potentially clean in-cabin PM_2.5. This study aims to find the PM_2.5 removal efficiency of portable air purifiers in a public transit bus. In various scenarios, after artificially preloading the in-cabin PM_2.5 concentration to 400 μg/m³, the concentrations were measured every 10 s, with and without the intervention of air purifiers. In a test bus with a volume of approximately 62.5 m³, three portable air purifiers were capable of reducing the average concentration of PM_2.5 by 42-74%, from 400 μg/m³, to levels below 15 μg/m³, the acceptable short-term exposure concentration recommended by WHO. When high concentrations of outdoor PM_2.5 entered the bus, purifiers maintained a relatively low level of in-cabin PM_2.5. Air purifiers were more effective in reducing in-cabin PM_2.5 than traditional air filtration and ventilation methods (air conditioning system filtration and door opening) in public transit buses. The deployed air purifiers reduced the concentration of particulate matter inside the bus, which may reduce the health risk of PM_2.5 exposure and the spreading of airborne infections in public transit, thus, implying the potential to enhance passengers' and drivers' health.

County-Based PM2.5 Concentrations’ Prediction and Its Relationship with Urban Landscape Pattern

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM2.5 values and landscape pattern. We achieved a cross validation R2 of 0.91 (RMSE = 9.06 μg/m3), 0.89 (RMSE = 10.19 μg/m3), and 0.90 (RMSE = 8.02 μg/m3) between the estimated and observed PM2.5 concentrations in 2016, 2018, and 2020, respectively. The PM2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of “Jiangsu > Shanghai > Zhejiang > Fujian” in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM2.5 (r = 0.84). There was also a significant correlation between the county-based PM2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM2.5 pollution in the study region.

Stratum Ventilation: Enabling Simultaneous Energy Conservation and Air Purification in Subway Cars

The supply of fresh air for underground rail transit systems is not as simple as opening windows, which is a conventional ventilation (CV) measure adopted in aboveground vehicles. This study aims to improve contaminant dilution and air purification in subway car ventilation systems and the safety of rail transit post-coronavirus disease pandemic era. We designed an air conditioning (AC) terminal system combined with stratum ventilation (SV) to enable energy consumption reduction for subway cars. We experimentally tested the effectiveness of a turbulence model to investigate ventilation in subway cars. Further, we compared the velocity fields of CV and SV in subway cars to understand the differences in their airflow organizations and contaminant removal efficiencies, along with the energy savings of four ventilation scenarios, based on the calculations carried out using computational fluid dynamics. At a ventilation flow rate of 7200 m³/h, the CO₂ concentration and temperature in the breathing areas of seated passengers were better in the SV than in the CV at a rate of 8500 m³/h. Additionally, the energy-saving rate of SV with AC cooling was 14.05%. The study provides new ideas for reducing the energy consumption of rail transit and broadens indoor application scenarios of SV technology.

Assessment of Factors Influencing Personal Exposure to Air Pollution on Main Roads in Bogota: A Mixed-Method Study

Background and Objectives: Particulate Matter (PM), particles of variable but small diameter can penetrate the respiratory system via inhalation, causing respiratory and/or cardiovascular diseases. This study aims to evaluate the association of environmental particulate matter (PM_2.5) and black carbon (BC) with respiratory health in users of different transport modes in four roads in Bogotá. Materials and Methods: this was a mixed-method study (including a cross sectional study and a qualitative description of the air quality perception), in 300 healthy participants, based on an exploratory sequential design. The respiratory effect was measured comparing the changes between pre- and post-spirometry. The PM_2.5 and black carbon (BC) concentrations were measured using portable devices. Inhaled doses were also calculated for each participant according to the mode and route. Perception was approached through semi-structured interviews. The analysis included multivariate models and concurrent triangulation. Results: The concentration of matter and black carbon were greater in bus users (median 50.67 µg m^-3; interquartile range (-IR): 306.7). We found greater inhaled dosages of air pollutants among bike users (16.41 µg m^-3). We did not find changes in the spirometry parameter associated with air pollutants or transport modes. The participants reported a major sensory influence at the visual and olfactory level as perception of bad air quality. Conclusions: We observed greater inhaled doses among active transport users. Nevertheless, no pathological changes were identified in the spirometry parameters. People's perceptions are a preponderant element in the assessment of air quality.

The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India

The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM_2.5 using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM_2.5 personal exposure of 133.9 (standard deviation = 114.8) μg/m³. The within-trip analysis showed weak inverse associations between greenspace markers and PM_2.5 concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM_2.5 during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM_2.5 of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM_2.5, which vary by season.

Impacts of Regional Speed Control Strategy Based on Macroscopic Fundamental Diagram on Energy Consumption and Traffic Emissions: A Case Study of Beijing

Numerous studies shown that particulate matter in the ambient environment has a significant impact on the health of the respiratory system. To understand the interrelationships between urban built environment, transportation operations and health, this study proposes an innovative approach that uses real-world GPS datasets to calculate energy consumption and emissions from transportation. The experiment used the traffic operation state in the Fourth Ring Road of Beijing as the research object and tested the impact of using the Regional speed optimization (RSO) strategy based on Macroscopic Fundamental Diagram (MFD) on energy consumption and emissions during peak hours. The impact of traffic emission on the health of roadside pedestrians is also considered. Changes in PM2.5 concentrations around four different built-up areas were calculated and compared. The computational experiments indicate that the PM2.5 pollutants exhausted by the traffic on the Ring Road during peak hours can reach up to 250 μg/m3, while the traffic emission on general roads near residential areas is only 50 μg/m3. Adopting Regional speed optimization can reduce the energy consumption of the road network by up to 18.8%. For roadside runners, the PM2.5 inhalation caused by night running in commercial and recreational areas is about 1.3-2.6 times that of night running in residential areas. Compared with morning or night running, the risk of respiratory disease caused by PM2.5 inhalation was about 10.3% higher than commuter running behavior. The research results provide a useful reference for energy conservation and emission reduction control strategies for different road types in cities and help existing cities to establish a traveler health evaluation system caused by traffic operation.

Pollutant specific optimal deep learning and statistical model building for air quality forecasting

Poor air quality is becoming a critical environmental concern in different countries over the last several years. Most of the air pollutants have serious consequences on human health and wellbeing. In this context, efficient forecasting of air pollutants is currently crucial to predict future events with a view to taking corrective actions and framing effective environmental policies. Although deep learning (DL) as well as statistical forecasting models are investigated in the literature, they have rarely used in air pollutant-specific optimal model building for long-term forecasting. In this paper, our aim is to develop the pollutant-specific optimal forecasting models for the phases spanning from preprocessing to model building by investigating a set of predictive techniques. In this regard, this paper presents a methodology for long-term forecasting of some important air pollutants. More specifically, a total of eight best performing models such as stacked LSTM, LSTM auto-encoder, Bi-LSTM, convLSTM, Holt-Winters, auto-regressive (AR), SARIMA, and Prophet are investigated for developing pollutant-specific optimal forecasting models. The study is carried out based on the real-world data obtained from government-run air quality monitoring units in Kolkata over a period of 4 years. The models such as Holt-Winters, Bi-LSTM, and ConvLSTM achieve high forecasting accuracy with respect to MAE and RMSE values for majority of the pollutants.

The effect of COVID-19 restrictions on particulate matter on different modes of transport in China

Since the COVID-19 pandemic, ventilation on transport has been improved to control the aerosol transmission. We utilized portable monitors to measure real-time concentrations of PM₁₀, PM_2.5, PM_1.0 and black carbon (BC) on six modes of transport and estimate personal exposures under the epidemic prevention. The mean concentrations of PM₁₀, PM_2.5, PM_1.0 and BC measured on transport were 18.8 ± 19.4, 16.6 ± 16.5, 12.2 ± 10.8 and 4.1 ± 6.9 μg/m³, respectively. It reduced PM levels on subway to apply the full fresh air mode rather than partial recirculation mode. Airplane had the lowest concentrations and the highest decay rates, implying the most efficient ventilation and filtration. PM were higher on intra-city transport than inter-city, and significantly increased on arrival at stations. BC and BC/PM ratios were higher on road transport than rail transport, indicating the contribution of exhaust emissions. The ventilation mode to exchange air with the outside and the positive association between concentrations and decay rates on high-speed train suggested filtration efficiency should be improved simultaneously with enhancing ventilation. Wearing facemasks on transport further protects passengers against PM exposure, which reduced personal exposure concentrations on four modes of transport lower than 10 μg/m³, the World Health Organization guideline.

The health effects of traffic-related air pollution: A review focused the health effects of going green

Traffic-related air pollution (TRAP) is global concern due to both the ecological damage of TRAP and the adverse health effects in Humans. Several strategies to reduce TRAP have been implemented, including the use of sustainable fuels, after-treatment technologies, and new energy vehicles. Such approaches can reduce the exhaust of particulate matter, adsorbed chemicals and a range of gases, but from a health perspective these approaches are not always successful. This review aims to discuss the approaches taken, and to then describe the likely health effects of these changes.