Annual, seasonal, and morning rush hour Land Use Regression models for black carbon in a school catchment area of Milan, Italy

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016-2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50-0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24-0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays'. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35-0.70; PM2.5 hourly R2 = 0.01-0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

Particulate matter in a motorcycle-dominated urban area: Source apportionment and cancer risk of lung deposited surface area (LDSA) concentrations

Source-apportioned particle concentrations are necessary to properly evaluate the health impacts of air pollution. In this study, a measurement station was established at an urban roadside in northern Taiwan to the investigate lung deposited surface area (LDSA) concentration, a relevant metric for the adverse health effects of aerosol exposure, along with PM₁ and equivalent black carbon (eBC) concentrations, particle number concentration (PNC), and particle size distribution (PSD). Through positive matrix factorization and multi-linear regression analysis, we attributed 57% of LDSA to traffic emissions over the entire study. During rush hour, the motorcycle fraction increased to 0.83 and LDSA (77.6 ± 9.9 µm²/cm³) and PNC (14,000 ± 2400 particles/cm³) values peaked, while 74% of LDSA was attributed to traffic. The LDSA ratio, defined as the ratio of measured LDSA to that estimated from the particle size distribution with a spherical assumption, also increased, highlighting the greater degree of fractal morphology during rush hour. The relationship between LDSA emitted by traffic and PNC yielded a higher r² (0.92) than the r² between traffic LDSA and eBC (0.82). Finally, the excess lifetime cancer risk linked with traffic emission was 1.56 × 10^-4 (i.e. 15.6 excess cancer cases for a population of 100,000 people) based on the LDSA apportionment results.

Living Lab Experience in Turin: Lifestyles and Exposure to Black Carbon

State-of-the-art, continuous personal monitoring is a reference point for assessing exposure to air pollution. European air-quality standards for particulate matter (PM) use mass concentration of PM (PM with aerodynamic diameters ≤ 10 μm (PM10) or ≤2.5 μm (PM2.5)) as the metric. It would be desirable to determine whether black carbon (BC) can be used as a better, newer indicator than PM10 and PM2.5. This article discusses the preliminary results of one of the three living laboratories developed in the project "Combination of traditional air quality indicators with an additional traffic proxy: Black Carbon (BC)". The Living Lab#1 (LL#1) involved 15 users in the city of Turin, Italy. Three portable aethalometers (AE51) were used to detect personal equivalent black carbon (eBC) concentrations in the respiratory area of volunteers at 10-s intervals as they went about their normal daily activities. The Geo-Tracker App and a longitudinal temporal activity diary were used to track users' movements. The sampling campaign was performed in November for one week. and each user was investigated for 24 h. A total of 8640 eBC measurements were obtained with an average daily personal exposure of 3.1 µg/m³ (±SD 1.3). The change in movement patterns and the variability of microenvironments were decisive determinants of exposure. Preliminary results highlight the potential utility of Living Labs to promote innovative approaches to design an urban-scale air-quality management plan which also includes BC as a new indicator.

Smoking habit in parents and exposure to environmental tobacco smoke in elementary school children of Milan

INTRODUCTION

Children with smoking parents are potentially exposed to Environmental Tobacco Smoke (ETS). The aims of this study were: 1) to assess ETS exposure in Milan schoolchildren, by measuring urinary cotinine (COT-U), 2) to compare the parents' perception of children ETS exposure, with the actual ETS exposure measured by COT-U, 3) to explore the factors influencing COT-U, including smoking bans at home, the season, and children characteristics.

METHODS

One-hundred school children (7-11 years) and their parents were recruited for the study in Spring 2018 (n = 81) and in Winter 2019 (n = 94), 75 children participated to both campaigns, for a sum of 175 observations. A questionnaire was submitted to parents to collect information about smoking habits in the house. COT-U was measured by LC-MS/MS in spot urine sample collected in the morning from children.

RESULTS

Detectable COT-U levels were found in 42% and 57% of children, in spring and winter, in contrast with 17% and 13% of parents acknowledging ETS exposure. Children living with smokers or e-cigarette users (vapers) (30% of the participants) had higher COT-U levels than children not living with smokers or vapers (median 0.67, 0.46, and <0.1 μg/L in spring, and 0.98, 0.85, and 0.11 μg/L in winter, respectively). Increasingly higher COT-U levels were observed in children living in homes where smoking was completely banned, allowed in the external parts of the home, or allowed in some rooms. The multiple regression analysis confirmed the positive significant effect of living with smokers, a partial smoking ban and absence of smoking ban at home, the winter season, and BMI as determinants of COT-U.

CONCLUSION

ETS exposure resulted in measurable urinary cotinine in children. Smoking parents underestimate exposure to ETS of their children. Living with smokers is a determinant of COT-U, only slightly mitigated by adopting partial smoking ban.

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.

Personal Exposure to Black Carbon at School and Levels of Fractional Exhaled Nitric Oxide in New York City

BACKGROUND

Schools are often located near traffic sources, leading to high levels of exposure to traffic-related air pollutants, including black carbon (BC). Thus, the school environment could play in a significant role in the adverse respiratory health of children.

OBJECTIVES

Our objective was to determine associations between personal BC levels at school and airway inflammation [i.e., fractional exhaled nitric oxide (FeNO)] in school-age children. We hypothesized that higher school BC (SBC) would be associated with higher FeNO.

METHODS

Children 9-14 years of age in New York City (NYC) (n=114) wore BC monitors for two 24-h periods over a 6-d sampling period, repeated 6 months later. SBC was defined as the average personal BC concentrations measured during NYC school hours (i.e., 0830-1430 hours). FeNO was measured following each 24-h BC monitoring period. Multivariable linear regression in generalized estimating equation models were used to examine associations between SBC and FeNO. Results are presented as percentage difference (PD) in FeNO.

RESULTS

Personal BC at school was associated with higher FeNO (PD=7.47% higher FeNO per 1-μg/m3 BC (95% CI: 1.31, 13.9), p=0.02]. Compared with BC exposure during school, a smaller PD in FeNO was observed in association with BC exposure while commuting to and from school [PD=6.82% (95% CI: 0.70, 13.3), p=0.03]. Personal BC in non-school environments and residential BC were not associated with FeNO (p>0.05). A significant association between personal BC at school and FeNO was observed among children with seroatopy who did not have asthma [PD=21.5% (95% CI: 4.81, 40.9), p=0.01].

DISCUSSION

Schools may be important sources of BC exposure that contribute to airway inflammation in school-age children. Our results provide rationale for interventions that target improved air quality in urban schools and classrooms. https://doi.org/10.1289/EHP8985.

Air quality around schools: Part I - A comprehensive literature review across high-income countries

Children are particularly vulnerable to the detrimental health impacts of poor air quality. In the UK, recent initiatives at local council level have focussed on mitigating children's air pollution exposure at school. However, an overview of the available evidence on concentration and exposure in school environments - and a summary of key knowledge gaps - has so far been lacking. To address this, we conducted a review bringing together recent academic and grey literature, relating to air quality in outdoor school environments - including playgrounds, drop-off zones, and the school commute - across high-income countries. We aimed to critically assess, synthesise, and categorise the available literature, to produce recommendations on future research and mitigating actions. Our searches initially identified 883 articles of interest, which were filtered down in screening and appraisal to a final total of 100 for inclusion. Many of the included studies focussed on nitrogen dioxide (NO₂), and particulate matter (PM) in both the coarse and fine fractions, around schools across a range of countries. Some studies also observed ozone (O₃) and volatile organic compounds (VOCs) outside schools. Our review identified evidence that children can encounter pollution peaks on the school journey, at school gates, and in school playgrounds; that nearby traffic is a key determinant of concentrations outside schools; and that factors relating to planning and urban design - such as the type of playground paving, and amount of surrounding green space - can influence school site concentrations. The review also outlines evidence gaps that can be targeted in future research. These include the need for more personal monitoring studies that distinguish between the exposure that takes place indoors and outdoors at school, and a need for a greater number of studies that conduct before-after evaluation of local interventions designed to mitigate children's exposure, such as green barriers and road closures. Finally, our review also proposes some tangible recommendations for policymakers and local leaders. The creation of clean air zones around schools; greening of school grounds; careful selection of new school sites; promotion of active travel to and from school; avoidance of major roads on the school commute; and scheduling of outdoor learning and play away from peak traffic hours, are all advocated by the evidence collated in this review.

Personal exposure to equivalent black carbon in children in Milan, Italy: Time-activity patterns and predictors by season

Air pollution is a global threat to public health, especially when considering susceptible populations, such as children. A better understanding of determinants of exposure could help epidemiologists in refining exposure assessment methods, and policy makers in identifying effective mitigation interventions. Through a participatory approach, 73 and 89 schoolchildren were involved in a two-season personal exposure monitoring campaign of equivalent black carbon (EBC) in Milan, Italy. GPS devices, time-activity diaries and a questionnaire were used to collect personal information. Exposure to EBC was 1.3 ± 1.5 μg/m³ and 3.9 ± 3.3 μg/m³ (mean ± sd) during the warm and the cold season, respectively. The highest peaks of exposure were detected during the home-to-school commute. Children received most of their daily dose at school and home (82%), but the highest dose/time intensity was related to transportation and outdoor environments. Linear mixed-effect models showed that meteorological variables were the most influencing predictors of personal exposure and inhaled dose, especially in the cold season. The total time spent in a car, duration of the home-to-school commute, and smoking habits of parents were important predictors as well. Our findings suggest that seasonality, time-activity and mobility patterns play an important role in explaining exposure patterns. Furthermore, by highlighting the contribution of traffic rush hours, transport-related microenvironments and traffic-related predictors, our study suggests that acting on a local scale could be an effective way of lowering personal exposure to EBC and inhaled dose of children in the city of Milan.

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.

Drivers of seasonal and annual air pollution exposure in a complex urban environment with multiple source contributions

Outdoor air pollution is a global health concern, but detailed exposure information is still limited for many parts of the world. In this study, high-resolution exposure surfaces were generated for annual and seasonal fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), and carbon monoxide (CO) for the Greater Beirut Area (GBA), Lebanon, an urban zone with a complex topography and multiple source contributions. Land use regression models (LUR) were calibrated and validated with monthly data collected from 58 locations between March 2017 and March 2018. The annual mean (±1 SD) concentrations of PM_2.5, PM₁₀, and CO across the monitoring locations were 68.1 (±15.7) μg/m³, 83.5 (±19.5) μg/m³, and 2.48 (±1.12) ppm, respectively. The coefficients of determination for LUR models ranged from 56 to 67% for PM_2.5, 44 to 63% for the PM₁₀ models, and 50 to 60% for the CO. LUR model structures varied significantly by season for both PM_2.5 and PM₁₀ but not for CO. Traffic emissions were consistently the main source of CO emissions throughout the year. The relative importance of industrial emissions and power generation sources towards predicted PM levels increased during the hot season while the contribution of the international airport diminished. Moreover, the complex topography of the study area along with the seasonal changes in the predominant wind directions affected the spatial predicted concentrations of all three pollutants. Overall, the predicted exposure surfaces were able to conserve the inter-pollution correlations determined from the field monitoring campaign, with the exception of the cold season. Our pollution surfaces suggest that the entire population of Beirut is regularly exposed to concentrations exceeding the World Health Organization (WHO) air quality standards for both PM_2.5 and PM₁₀.

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.

Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?

Land Use Regression (LUR) modeling is a widely used technique to model the spatial variability of air pollutants in epidemiology. In this study, we explore whether a LUR model can predict home-to-school commuting exposure to black carbon (BC). During January and February 2019, 43 children walking to school were involved in a personal monitoring campaign measuring exposure to BC and tracking their home-to-school routes. At the same time, a previously developed LUR model for the study area was applied to estimate BC exposure on points along the route. Personal BC exposure varied widely with mean ± SD of 9003 ± 4864 ng/m3. The comparison between the two methods showed good agreement (Pearson’s r = 0.74, Lin’s Concordance Correlation Coefficient = 0.6), suggesting that LUR estimates are capable of catching differences among routes and predicting the cleanest route. However, the model tends to underestimate absolute concentrations by 29% on average. A LUR model can be useful in predicting personal exposure and can help urban planners in Milan to build a healthier city for schoolchildren by promoting less polluted home-to-school routes.