Spatial characteristics and determinants of in-traffic black carbon in Shanghai, China: Combination of mobile monitoring and land use regression model

High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP). The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am-10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-h measurements over two-week periods. Generalized Additive Models (GAMs), which combined data on pollution concentrations with spatial, temporal, and spatiotemporal predictor variables were used to model and predict concentration surfaces. Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54-0.86). Varying spatial patterns were observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of 'n visits' (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant. This study provides a single source of exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Ambient black carbon variations and emission characteristics of typical Chinese vessels in the Yangtze River Delta, China

Black carbon (BC) has a significant impact on air quality, climate change, and human health. Studies on BC from vessel exhaust have been focused on in recent years. To realize the contribution of BC from vessels to ambient air quality, 28 months of BC variation were observed from February 2019 to May 2022, including 3 fishing moratoriums and 2 normal periods. The results showed that the average daily concentration of BC in the fishing moratorium was significantly lower than that in the normal period. The difference proportion of the BC concentration between 370 and 880 nm was calculated over the whole period. As a result, the mean difference value in the fishing moratorium from February to May was 0.06 ± 0.07, and the normal period was -0.02 ± 0.05. The aethalometer model indicated that BC was greatly affected by fossil fuel combustion in the normal period. The effect of vessel emissions on regional BC concentrations was considerable. In addition, 16 PAHs and 21 elements in PM emitted from 24 vessels of different types were sampled and analyzed in Dianshan Lake and the Taipu River. EC accounted for the highest proportion (23.64%) in the sample of small trawlers compared to the emissions from cargo ships with large tonnages. The component profiles of vessel exhaust showed that Zn, As, phenanthrene (Phe), anthracene (Ant), fluoranthene (Fla), and pyrene (Pyr) were the dominant species, although some of these species were mainly recognized as characteristic factors of coal combustion. To improve the accuracy of identifying the vessel source, the diagnostic ratios of Ant/(Ant + Phe), BaA/(BaA + Chr), Phe/Ant, and BaA/Chr were provided, and they exhibited the obvious characteristics of fuel combustion.

Nearly five-year continuous atmospheric measurements of black carbon over a suburban area in central France

Atmospheric black carbon (BC) concentration over a nearly 5 year period (mid-2017-2021) was continuously monitored over a suburban area of Orléans city (France). Annual mean atmospheric BC concentration were 0.75 ± 0.65, 0.58 ± 0.44, 0.54 ± 0.64, 0.48 ± 0.46 and 0.50 ± 0.72 μg m^-3, respectively, for the year of 2017, 2018, 2019, 2020 and 2021. Seasonal pattern was also observed with maximum concentration (0.70 ± 0.18 μg m^-3) in winter and minimum concentration (0.38 ± 0.04 μg m^-3) in summer. We found a different diurnal pattern between cold (winter and fall) and warm (spring and summer) seasons. Further, fossil fuel burning contributed >90 % of atmospheric BC in the summer and biomass burning had a contribution equivalent to that of the fossil fuel in the winter. Significant week days effect on BC concentrations was observed, indicating the important role of local emissions such as car exhaust in BC level at this site. The behavior of atmospheric BC level with COVID-19 lockdown was also analyzed. We found that during the lockdown in warm season (first lockdown: 27 March-10 May 2020 and third lockdown 17 March-3 May 2021) BC concentration were lower than in cold season (second lockdown: 29 October-15 December 2020), which could be mainly related to the BC emission from biomass burning for heating. This study provides a long-term BC measurement database input for air quality and climate models. The analysis of especially weekend and lockdown effect showed implications on future policymaking toward improving local and regional air quality as well.

Locally weighted total least-squares variance component estimation for modeling urban air pollution

Land use regression (LUR) models are one of the standard methods for estimating air pollution concentration in urban areas. These models are usually low accurate due to inappropriate stochastic models (weight matrix). Furthermore, the measurement or modeling of dependent and independent variables used in LUR models is affected by various errors, which indicates the need to use an efficient stochastic and functional model to achieve the best estimation. This study proposes a locally weighted total least-squares variance component estimation (LW-TLS-VCE) for modeling urban air pollution. In the proposed method, in the first step, a locally weighted total least-squares (LW-TLS) regression is developed to simultaneously considers the non-stationary effects and errors of dependent and independent variables. In the second step, the variance components of the stochastic model are estimated to achieve the best linear unbiased estimation of unknowns. The efficiency of the proposed method is evaluated by modeling PM_2.5 concentrations via meteorological, land use, and traffic variables in Isfahan, Iran. The benefits provided by the proposed method, including considering non-stationary effects and random errors of all variables, besides estimating the actual variance of observations, are evaluated by comparing four consecutive methods. The obtained results demonstrate that using a suitable stochastic and functional model will significantly increase the proposed method's efficiency in PM_2.5 modeling.

Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM_2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM_2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM_2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM_2.5 were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM_2.5 ranged from 13.51 to 15.40 μg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R² of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R² of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM_2.5 exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

Atmospheric black carbon in urban and traffic areas in Shanghai: Temporal variations, source characteristics, and population exposure

Black carbon (BC) measurements were performed at Pudong (PD) urban supersite and Gonghexin (GH) roadside station from December 1, 2017 to August 10, 2020 to investigate the variations, source characteristics, and population exposure levels of BC in traffic and urban areas in Shanghai, China. The BC median concentration at GH was more than two-fold that at PD. Absorption Ångström exponent (AAE) values were 1.27 ± 0.17 and 1.31 ± 0.17 at PD and GH, respectively, suggesting the dominance of liquid fossil fuel combustion sources (i.e., traffic exhaust) at these stations. The higher BC and AAE values in winter at PD indicated the relatively increasing contribution of solid fuels (i.e., biomass burning) to BC concentration in urban Shanghai. The diurnal variation in BC showed similar twin-peak patterns at PD and GH, implying that traffic emission mainly contributed to ambient BC concentration in urban Shanghai. The estimated daily intakes (EDIs) of BC were generally higher in males than in females at both PD and GH. The highest BC EDIs at PD were found in age subgroups 1-<2 and 2-<3 years. In contrast, the BC EDIs at GH were observed in age subgroups 6-<9, 12-<15, and 15-<18 years, which were higher than those determined at PD, indicating that more attention must be paid to BC exposure of the population in these age subgroups. These results provide scientific insights into variations, source characteristics, and population exposure levels of BC in urban and traffic areas and could help in the development of BC control strategies in Shanghai.

Development of season-dependent land use regression models to estimate BC and PM1 exposure

Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM₁) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM₁ based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM₁. The mean concentrations of pollutants were higher in winter (BC: 4.72 μg/m³; PM₁: 56.97 μg/m³) than in fall (BC: 3.74 μg/m³; PM₁: 33.29 μg/m³) and summer (BC: 2.77 μg/m³; PM₁: 27.04 μg/m³). For both BC and PM₁, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R² of the LUR models for BC and PM₁ ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic-related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM₁ models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM₁ was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM₁ in typical inner-city environments, and support pollution management.

Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion

Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R² from monthly estimates at fixed sites by 38%. After revising emissions through inverse dispersion modeling, we combine this model with stationary/mobile measurements through Bayesian Maximum Entropy (I-DISP BME) to produce temporally coarse yet spatially fine data fusion. We compare this novel data fusion approach to BME using only measurements (Flat BME). A 10-fold conventional cross-validation (representative of months with mobile measurements) shows that all BME methods have R² values that range from 0.787 to 0.798. A 2-fold cross-validation (representative of months with no mobile measurements) shows that the R² for I-DISP BME increases by a factor 90 when compared to Flat BME. Furthermore, not only is our novel I-DISP BME method more accurate than the classic Flat BME method, but the area it detects as highly exposed can be up to 5 times larger than that detected by the less accurate Flat BME method.

Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt

This study harnesses the power of mobile data in developing a spatial model for predicting black carbon (BC) concentrations within one of the most heavily populated regions in the Middle East and North Africa MENA region, Greater Cairo Region (GCR) in Egypt. A mobile data collection campaign was conducted in GCR to collect BC measurements along specific travel routes. In total, 3,300 km were travelled across a widespread 525 km of routes. Reported average BC values were around 20 µg/m³, announcing an alarming order of magnitude value when compared to the maximum reported values in similar studies. A bi-directional stepwise land use regression (LUR) model was developed to select the best combination of explanatory variables and generate an exposure surface for BC, in addition to a number of machine learning models (random forest gradient boost, light gradient boost model (LightGBM), Keras neural network (NN)). Data from 7 air quality (AQ) stations were compared-in terms of mean square error (MSE) and mean absolute error (MAE)-with predictions from the LUR and the NN model. The NN model estimated higher BC concentrations in the downtown areas, while lower concentrations are estimated for the peripheral area at the east side of the city. Such results shed light on the credibility of the LUR models in generating a general spatial trend of BC concentrations while the superiority of NN in BC accuracy estimation (0.023 vs 0.241 in terms of MSE and 0.12 vs 0.389 in terms of MAE; of NN vs LUR respectively).

Air pollution concentration and period of the day modulates inhalation of PM2.5 during moderate- and high-intensity interval exercise

The increase in minute ventilation during exercise led to higher inhalation of air pollution and, consequently, to exacerbation of health issues. Therefore, the intensity of exercise and the air pollution concentration of the environment could be determinant variables to poor outcomes. This study aimed to investigate the inhaled dose of particulate matter 2.5 (PM_2.5) during a moderate- and high-intensity interval exercise session performed in the morning and evening at different locations of Porto Alegre City. Eighteen individuals performed a cardiopulmonary exercise test, a moderate-intensity interval exercise (MIIE), and a high-intensity interval exercise (HIIE). Heart rate was monitored to estimate minute ventilation and total ventilation of the session. The concentration of PM_2.5 was measured during the morning (6-8a.m.) and evening (6-8p.m.) by fixed-site monitors placed at five points of Porto Alegre City. The PM_2.5 inhalation during MIIE and HIIE performed in the morning and evening in the monitoring points was estimated. HIIE showed higher minute ventilation (VE) (p = 0.0048) and total ventilation did not differ between groups (p = 0.4648). PM_2.5 concentrations were higher during the mornings (p < 0.001). Monitored point 1 had higher levels of PM_2.5 in the morning and evening (p < 0.001). The inhalation of PM_2.5 in the morning showed no difference in MIIE (p = 0.8172) and HIIE (p = 0.7306) groups among the points. In the evening, the inhalation of PM_2.5 was higher in point 1 in MIIE and HIIE group (p < 0.001). MIIE and HIIE had higher inhalation of PM_2.5 in the morning than in the evening (p < 0.001). Total ventilation of exercise is a crucial factor that contributes to the inhalation dose of air pollution.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R²) for the LUR models ranged from 0.53-0.87 (PM_2.5), 0.53-0.85 (PM₁₀), and 0.33-0.67 (NO₂). The performance of the daytime/nighttime LUR models for PM_2.5 and PM₁₀ was better than that of the full-day models according to the results of model adjusted R² and validation R². Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO₂ to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM_2.5/PM₁₀ has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM_2.5 and PM₁₀. Nighttime could be a critical exposure period, due to high pollutant concentrations.

Assessing schoolchildren's exposure to air pollution during the daily commute - A systematic review

Air pollution is mostly caused by emissions from human activities, and exposure to air pollution is linked with numerous adverse human health outcomes. Recent studies have identified that although people only spend a small proportion of time on their daily commutes, the commuter microenvironment is a significant contributor to their total daily air pollution exposure. Schoolchildren are a particularly vulnerable cohort of the population, and their exposure to air pollution at home or school has been documented in a number of case studies. A few studies have identified that schoolchildren's exposure during commutes is linked with adverse cognitive outcomes and severe wheeze in asthmatic children. However, the determinants of total exposure, such as route choice and commute mode, and their subsequent health impacts on schoolchildren are still not well-understood. The aim of this paper is to review and synthesize recent studies on assessing schoolchildren's exposure to various air pollutants during the daily commute. Through reviewing 31 relevant studies published between 2004 and 2020, we tried to identify consistent patterns, trends, and underlying causal factors in the results. These studies were carried out across 10 commute modes and 12 different air pollutants. Air pollution in cities is highly heterogeneous in time and space, and commuting schoolchildren move through the urban area in complex ways. Measurements from fixed monitoring stations (FMSs), personal monitoring, and air quality modeling are the three most common approaches to determining exposure to ambient air pollutant concentrations. The time-activity diary (TAD), GPS tracker, online route collection app, and GIS-based route simulation are four widely used methods to determine schoolchildren's daily commuting routes. We found that route choices exerted a determining impact on schoolchildren's exposure. It is challenging to rank commute modes in order of exposure, as each scenario has numerous uncontrollable determinants, and there are notable research gaps. We suggest that future studies should concentrate on examining exposure patterns of schoolchildren in developing countries, exposure in the subway and trains, investigating the reliability of current simulation methods, exploring the environmental justice issue, and identifying the health impacts during commuting. It is recommended that three promising tools of smartphones, data fusion, and GIS should be widely used to overcome the challenges encountered in scaling up commuter exposure studies to population scales.

Integration of air pollution data collected by mobile measurement to derive a preliminary spatiotemporal air pollution profile from two neighboring German-Czech border villages

Generally, there are only a few fixed air quality monitoring stations installed in villages or rural areas and only a few studies on small-scale variations in air pollution have been described in detail, which make it difficult to estimate human exposure in such environments and related adverse health effects. Moreover, biomass combustion can be an important source of air pollution in rural areas, comparable to vehicle and industrial emissions in urban planning. And their air pollutants are mainly affected by local sources. For this reason, a survey on rural air pollution was carried out in this study. Therefore, portable, battery-powered monitoring devices were used to measure particulate matter (PM₁₀, PM_2.5, PM₁, particle number concentration, and black carbon) in order to study air quality in rural communities. The focus of the investigations was to explore the application of mobile monitoring equipment in small-scale environments, compare the differences in rural air pollutants between two neighboring villages in two countries, and the identification of pollution hotspots. The measurements were carried out in November 2018 in two villages on the German-Czech border. Over a period of four days, 21 mobile measurements along fixed routes were carried out simultaneously at both locations. The analysis of the data revealed significant differences in PN and PM concentrations in rural air pollutants between the two countries. The spatial and temporal distribution of air pollution hotspots in the Czech village was higher than that in the German village. The relationships between the measurement parameters were weak but highly significant and the meteorological parameters can effect air pollution. Overall, the results of this study show that mobile measurements are suitable for effectively recording and distinguishing spatial and temporal characteristics of air quality.

The characteristics and local-regional contributions of atmospheric black carbon over urban and suburban locations in Shanghai, China

Black carbon (BC), produced from the incomplete combustion of carbonaceous fuels, has emerged as a major contributor to global climate change with adverse health effects. Based on one-year (2016.06.01-2017.06.30) equivalent black carbon (eBC) measurements, this study analyzed the characteristics of eBC concentrations and the local-regional contributions at an urban site (Pudong, PD) and a suburban site (Qingpu, QP) in Shanghai, China. The results showed that the annual average eBC concentrations were 1.17 ± 0.61 μg m^-3 and 2.09 ± 0.97 μg m^-3 at PD and QP, respectively. The high eBC concentrations occurred in winter and at weekends both for PD and QP. There were significant negative correlation coefficients between the daily eBC, the daily wind speed (WS) and the daily boundary layer height (BLH) at PD (r_ws: 0.45, r_blh = -0.35, p < 0.01) and QP (r_ws: 0.49, r_blh = -0.32, p < 0.01). And the relative higher eBC concentrations coincided with southerly, southwesterly and westerly winds although these winds had lower frequencies. This could be related to the agricultural fire in these directions during summer harvesttime. The significant partial correlation coefficients of eBC-CO (r_u:0.37-0.64, r_s:0.18-0.44, p < 0.01) and eBC-NO₂ (r_u:0.49-0.74, r_s:0.38-0.75, p < 0.01) could suggest that eBC mainly come from vehicular exhaust emissions in Shanghai. Besides, the higher eBC/PM_2.5 (5.29% ± 1.94%) and eBC/CO(0.30% ± 0.14%) at QP indicated the more combustion activities and diesel-powered vehicle emissions in suburban areas. The concentration weighted trajectory (CWT) analysis indicated that the surrounding areas at the junction of Shanghai, Jiangsu, and Zhejiang provinces seemed to be relatively the most important sources outside of Shanghai.

Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models

Black carbon (BC) is an important component of particulate matter (PM) in urban environments. BC is typically emitted from gas and diesel engines, coal-fired power plants, and other sources that burn fossil fuel. In contrast to PM, BC measurements are not always available on a large scale due to the operational cost and complexity of the instrumentation. Therefore, it is advantageous to develop a mathematical model for estimating the quantity of BC in the air, termed a BC proxy, to enable widening of spatial air pollution mapping. This article presents the development of BC proxies based on a Bayesian framework using measurements of PM concentrations and size distributions from 10 to 10,000 nm from a recent mobile air pollution study across several areas of Jordan. Bayesian methods using informative priors can naturally prevent over-fitting in the modelling process and the methods generate a confidence interval around the prediction, thus the estimated BC concentration can be directly quantified and assessed. In particular, two types of models are developed based on their transparency and interpretability, referred to as white-box and black-box models. The proposed methods are tested on extensive data sets obtained from the measurement campaign in Jordan. In this study, black-box models perform slightly better due to their model complexity. Nevertheless, the results demonstrate that the performance of both models does not differ significantly. In practice, white-box models are relatively more convenient to be deployed, the methods are well understood by scientists, and the models can be used to better understand key relationships.

Land use regression for spatial distribution of urban particulate matter (PM10) and sulfur dioxide (SO2) in a heavily polluted city in Northeast China

Particulate material 10 μm (PM₁₀) and sulfur dioxide (SO₂) are representative air pollutants in Northeast China and may contribute more to the morbidity of respiratory and cardiovascular disease than may other pollutants. Up to now, there have been few studies on the relation between health effect and air pollution by PM₁₀ and SO₂ in Northeast China, which may be due to the lack of a model for determination of air pollution exposure. For the first time, we used daily concentration data and influencing factors (different type of land use, road length and population density, and weather conditions as well) to develop land use regression models for spatial distribution of PM₁₀ and SO₂ in a central city in Northeast China in both heating and non-heating months. The final models of SO₂ and PM₁₀ estimation showed good performance (heating months: R² = 0.88 for SO₂, R² = 0.88 for PM₁₀; non-heating months: R² = 0.79 for SO₂; R² = 0.87 for PM₁₀). Estimated concentrations of air pollutants were more affected by population density in heating seasons and land use area in non-heating seasons. We used the land use regression (LUR) models developed to predict pollutant levels in nine districts in Shenyang and conducted a correlation analysis between air pollutant levels and hospital admission rates for childhood asthma. There were high associations between asthma hospital admission rates and air pollution levels of SO₂ and PM₁₀, which indicated the usability of the LUR models and the need for more concern about the health effects of SO₂ and PM₁₀ in Northeast China. This study may contribute to epidemiological research on the relation between air pollutant exposure and typical chronic disease in Northeast China as well as providing the government with more scientific recommendations for air pollution prevention.

Modelling urban cyclists' exposure to black carbon particles using high spatiotemporal data: A statistical approach

This is a pioneering work in South America to model the exposure of cyclists to black carbon (BC) while riding in an urban area with high spatiotemporal variability of BC concentrations. We report on mobile BC concentrations sampled on 10 biking sessions in the city of Curitiba (Brazil), during rush hours of weekdays, covering four routes and totaling 178 km. Moreover, simultaneous BC measurements were conducted within a street canyon (street and rooftop levels) and at a site located 13 km from the city center. We used two statistical approaches to model the BC concentrations: multiple linear regression (MLR) and a machine-learning technique called random forests (RF). A pool of 25 candidate variables was created, including pollution measurements, traffic characteristics, street geometry and meteorology. The aggregated mean BC concentration within 30-m buffers along the four routes was 7.09 μg m^-3, with large spatial variability (5th and 95th percentiles of 1.75 and 16.83 μg m^-3, respectively). On average, the concentrations at the street canyon façade (5 m height) were lower than the mobile data but higher than the urban background levels. The MLR model explained a low percentage of variance (24%), but was within the values found in the literature for on-road BC mobile data. RF explained a larger variance (54%) with the additional advantage of having lower requirements for the target and predictor variables. The most impactful predictor for both models was the traffic rate of heavy-duty vehicles. Thus, to reduce the BC exposure of cyclists and residents living close to busy streets, we emphasize the importance of renewing and/or retrofitting the diesel-powered fleet, particularly public buses with old vehicle technologies. Urban planners could also use this valuable information to project bicycle lanes with greater separation from the circulation of heavy-duty diesel vehicles.

Roadside Exposure and Inflammation Biomarkers among a Cohort of Traffic Police in Kathmandu, Nepal

Air pollution is a major environmental problem in the Kathmandu Valley. Specifically, roadside and traffic-related air pollution exposure levels were found at very high levels exceeding Nepal air quality standards for daily PM_2.5. In an exposure study involving traffic police officers, we collected 78 blood samples in a highly polluted spring season (16 February 2014–4 April 2014) and 63 blood samples in the less polluted summer season (20 July 2014–22 August 2014). Fourteen biomarkers, i.e., C-reactive protein (CRP), serum amyloid A (SAA), intracellular adhesion molecule (ICAM-1), vascular cell adhesion molecule (VCAM-1), interferon gamma (IFN-γ), interleukins (IL1-β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-13), and tumor necrosis factor (TNF-α) were analyzed in collected blood samples using proinflammatory panel 1 kits and vascular injury panel 2 kits. All the inflammatory biomarker levels were higher in the summer season than in the spring season, while particulate levels were higher in the spring season than in the summer season. We did not find significant association between 24-hour average PM_2.5 or black carbon (BC) exposure levels with most of analyzed biomarkers for the traffic volunteers working and residing near busy roads in Kathmandu, Nepal, during 2014. Inflammation and vascular injury marker concentrations were generally higher in females, suggesting the important role of gender in inflammation biomarkers. Because of the small sample size of female subjects, further investigation with a larger sample size is required to confirm the role of gender in inflammation biomarkers.