High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China

BACKGROUND

Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution.

OBJECTIVE

This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides.

METHOD

A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method.

RESULT

The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm^-3. The 10-fold cross validation R² reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NO_x emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas.

CONCLUSION

We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors.

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).

The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: Implications for the public health benefits of PM2.5 reduction

Understanding the intra-city variation of PM_2.5 is important for air quality management and exposure assessment. In this study, to investigate the spatiotemporal variation of PM_2.5 in Guangzhou, we developed land use regression (LUR) models using data from 49 routine air quality monitoring stations. The R², adjust R² and 10-fold cross validation R² for the annual PM_2.5 LUR model were 0.78, 0.72 and 0.66, respectively, indicating the robustness of the model. In all the LUR models, traffic variables (e.g., length of main road and the distance to nearest ancillary) were the most common variables in the LUR models, suggesting vehicle emission was the most important contributor to PM_2.5 and controlling vehicle emissions would be an effective way to reduce PM_2.5. The predicted PM_2.5 exhibited significant variations with different land uses, with the highest value for impervious surfaces, followed by green land, cropland, forest and water areas. Guangzhou as the third largest city that PM_2.5 concentration has achieved CAAQS Grade II guideline in China, it represents a useful case study city to examine the health and economic benefits of further reduction of PM_2.5 to the lower concentration ranges. So, the health and economic benefits of reducing PM_2.5 in Guangzhou was further estimated using the BenMAP model, based on the annual PM_2.5 concentration predicted by the LUR model. The results showed that the avoided all cause mortalities were 992 cases (95% CI: 221-2140) and the corresponding economic benefits were 1478 million CNY (95% CI: 257-2524) (willingness to pay approach) if the annual PM_2.5 concentration can be reduced to the annual CAAQS Grade I guideline value of 15 μg/m³. Our results are expected to provide valuable information for further air pollution control strategies in China.

Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas

As one of the principal components of haze, fine particulate matter (PM_2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM_2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM_2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM_2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM_2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.

Polycyclic aromatic hydrocarbons in the soils of the Yangtze River Delta Urban Agglomeration, China: Influence of land cover types and urbanization

With the development of urbanization, urban areas have become the main sources and sinks of polycyclic aromatic hydrocarbons (PAHs). The effects of human activities on the behaviors of PAHs in urban agglomerations have attracted significant attention. We collected soil samples (n = 330) to investigate the distribution, composition, and sources of 16 PAHs in the Yangtze River Delta Urban Agglomeration using the land resolution of 24 km × 24 km. The concentrations of Σ₁₆PAHs ranged from 21 to 2034 ng/g, with a median value of 124 ± 338 ng/g. The concentrations of PAHs were highest in impervious surfaces (350 ± 352 ng/g), followed by grassland (259 ± 322 ng/g), cropland (254 ± 341 ng/g), forest (190 ± 303 ng/g), and water (68 ± 34 ng/g). PAHs were dominated by medium-molecular-weight components (4 rings PAHs), followed by PAHs with high-molecular-weight (5-6 rings PAHs) and low-molecular-weight (2-3 rings PAHs) components. Fluoranthene, benzo[a]anthracene and chrysene are three major pollutants in YRDUA. A positive matrix factorization model indicated that fossil fuel combustion, coal combustion and volatilization, vehicle emission, and biomass burning were the main sources of PAHs, contributing 36%, 29%, 22%, and 12% of PAH sources, respectively. Urbanization parameters were positively correlated with PAH concentrations. A land use regression (LUR) model integrated with urbanization parameters showed evidence of the strong relationship between measured PAHs and predicted PAHs. These findings together highlighted that land cover types and human activities intensively influenced the PAHs pollution in the highly urbanized zones.

Application of land use regression to assess exposure and identify potential sources in PM2.5, BC, NO2 concentrations

BACKGROUND

Understanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data.

OBJECTIVE

Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM_2.5), black carbon (BC) and nitrogen dioxide (NO₂) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study.

METHOD

Two one-week integrated samples were collected at 30 PM_2.5 (BC) sites and 45 NO₂ sites in each two distinct seasons. Samples of 1/3 of the sites were collected simultaneously. Annual adjusted average was calculated and regressed against pre-selected GIS-derived predictor variables in a multivariate regression model.

RESULTS

LUR explained 65% of the spatial variability in PM_2.5, 78% in BC and 73% in NO₂. Mean (±Standard Deviation) of predicted PM_2.5, BC and NO₂ exposure levels were 48.3 (±6.3) μg/m³, 7.5 (±1.4) μg/m³ and 27.3 (±8.2) μg/m³, respectively. Weak spatial corrections (Pearson r = 0.05-0.25) among three pollutants were observed, indicating the presence of different sources. Regression results showed that PM_2.5, BC and NO₂ levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO₂ levels were associated with larger industrial land use. The three pollutants were correlated with sources at a scale of ≤5 km and even smaller scales (100-700m) were found for BC and NO₂.

CONCLUSION

We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM_2.5, NO₂ and BC concentrations in urban/rural settings of China. Our findings highlighted important contributors to within-city heterogeneity in outdoor-generated exposure, and indicated traffic, industry and agriculture may significantly contribute to PM_2.5, NO₂ and BC concentrations.

A spatiotemporal land-use-regression model to assess individual level long-term exposure to ambient fine particulate matters

We aimed to establish a spatiotemporal land-use-regression (ST-LUR) model assessing individual level long-term exposure to fine particulate matters (PM_2.5) among 6627 adults enrolled in Guangdong province, China from 2015 to 2016. We collected weekly average PM_2.5 concentration (from the air quality monitoring stations) and visibility, population density, road density and types of land use of each air quality monitoring station and participant’s residential address from April 2013 to December 2016. A ST-LUR model was established using these spatiotemporal data, and was employed to estimate the weekly average PM_2.5 concentration of each individual residential address. Data analysis was carried out by R software (version 3.5.1) and the SpatioTemporal package was used. The results showed that the ST-LUR model applying the land use data extracted using a buffer radius of 1300 m had the best modelling fitness. The results of 10-fold cross validation showed that the R² was 88.86% and the RMSE (Root mean square error) was 5.65 μg/m³. The two-year average of PM_2.5 prior to the date of investigation were calculated for each participant. This study provided a novel method to precisely assess individual level long-term exposure to ambient PM_2.5, which may extend our understanding on the health impacts of air pollution.

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Variables input in the spatiotemporal land-use-regression (ST-LUR) model include visibility, population density, road density, and types of land use.

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The land use data should be extracted using a buffer radius of 1300 m.

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The R² of the ST-LUR model was 88.86% and the RMSE was 5.65 μg/m³, indicating the good performance of the model.

Air pollution exposure in association with maternal thyroid function during early pregnancy

Association of prenatal air pollution exposure with maternal thyroid hormone (TH) levels remains unclear, especially during early pregnancy when even small changes in maternal TH could affect fetal neurodevelopment. We examined the effect of air pollution exposure on maternal TH levels in the second trimester of pregnancy. Serum concentrations of free thyroxine (FT4) and thyroid-stimulating hormone (TSH) in 8077 pregnant women were measured by fluorescence and chemiluminescence immunoassays. Prenatal exposure to fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) was estimated using land use regression models. FT4 levels were significantly inversely associated with both PM_2.5 and NO₂ exposure. A 10 μg/m³ increase in NO₂ exposure in first trimester and PM_2.5 exposure in second trimester was associated with 0.61% (95% CI, -0.88% to -0.35%) and 0.73% (95% CI, -1.25% to -0.20%) decrease in FT4 levels, respectively. PM_2.5 exposure was also associated with elevated odds of maternal hypothyroxinemia. A 10 μg/m³ increase in PM_2.5 exposure in both first and second trimester was associated with 28% (OR = 1.28, 95% CI, 1.05-1.57) and 23% (OR = 1.23, 95% CI, 1.00-1.51) increase in the odds of maternal hypothyroxinemia, respectively. Our findings suggest that air pollution may interfere with maternal thyroid function during early pregnancy.

Spatial-temporal variations of summertime ozone concentrations across a metropolitan area using a network of low-cost monitors to develop 24 hourly land-use regression models

Ten relatively-low-cost ozone monitors (Aeroqual Series 500 with OZL ozone sensor) were deployed to assess the spatial and temporal variability of ambient ozone concentrations across residential areas in the Monroe County, New York from June to October 2017. The monitors were calibrated in the laboratory and then deployed to a local air quality monitoring site where they were compared to the federal equivalent method values. These correlations were used to correct the measured ozone concentrations. The values were also used to develop hourly land use regression models (LUR) based on the deletion/substitution/addition (D/S/A) algorithm that can be used to predict the spatial and temporal concentrations of ozone at any hour of a summertime day and given location in Monroe County. Adjusted R2 values were high (average 0.83) with the highest adjusted R2 for the model between 8 and 9 AM (i.e. 1-2 h after the peak of primary emissions during the morning rush hours). Spatial predictors with the highest positive effects on ozone estimates were high intensity developed areas, low and medium intensity developed areas, forests + shrubs, average elevation, Interstate + highways, and the annual average vehicular daily traffic counts. These predictors are associated with potential emissions of anthropogenic and biogenic precursors. Maps developed from the models exhibited reasonable spatial and temporal patterns, with low ozone concentrations overnight and the highest concentrations between 11 AM and 5 PM. The adjusted R2 between the model predictions and the measured values varied between 0.79 and 0.87 (mean = 0.83). The combined use of the network of low-cost monitors and LUR modeling provide useful estimates of intraurban ozone variability and exposure estimates that will be used in future epidemiological studies.

Nitrogen dioxide air pollution and preterm birth in Shanghai, China

BACKGROUND

Nitrogen dioxide (NO₂) is a typical indicator of traffic-related air pollution, and few studies with exposure assessment of high resolution have been conducted to explore its association with preterm birth in China.

OBJECTIVES

To investigate the association between NO₂ exposure based on a land use regression (LUR) model and preterm birth in Shanghai, China.

METHODS

A retrospective cohort study was performed among 25,493 singleton pregnancies in a major maternity hospital in Shanghai, China, from 2014 to 2015. A temporally adjusted LUR model was used to predict the prenatal exposure to NO₂ based on residence address of each gravida. Logistic regression was performed to evaluate the associations of ambient NO₂ exposure with preterm birth during six exposure periods, including the entire pregnancy, the first trimester, the second trimester, the third trimester, the last month, and the last week before delivery. Sensitivity analysis with a matched case-control design was conducted to test the robustness of the association between NO₂ exposure and preterm birth.

RESULTS

The average NO₂ concentrations during the entire pregnancy was 48.23 µg/m³ among all participants. A 10 µg/m³ increase in NO₂ concentrations was associated with preterm birth, with an adjusted odds ratio of 1.03 (95% confidence interval [CI]: 0.96,1.10) for exposures during the entire pregnancy, 1.00 (95%CI: 0.95,1.06) in the first trimester, 1.01 (95%CI: 0.96,1.07) in the second trimester, 1.07 (95%CI: 1.02,1.13) in the third trimester, 1.10 (95%CI: 1.04,1.15) and 1.05 (95%CI: 1.00,1.09) in the month and week before delivery, respectively. The results of the matched case-control analysis were generally consistent with those of main analyses.

CONCLUSION

NO₂ may increase the risk of preterm birth, especially for exposures during the third trimester, the month and the week before delivery in Shanghai, China.

Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model

Accurate spatial information of PM_2.5 is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM_2.5. However, the predicted PM_2.5 spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km × 1 km MAIAC AOD and observed ground PM_2.5 in Beijing, and then seasonal PM_2.5 maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM_2.5 maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM_2.5 concentration at seasonal level (R² ≥ 0.53 in model fitting and testing), which was comparable with the capability of LUR models (R² ≥ 0.81 in model fitting and testing). PM_2.5 maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM_2.5. Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing.

Combining land use regression models and fixed site monitoring to reconstruct spatiotemporal variability of NO2 concentrations over a wide geographical area

The epidemiological research benefits from an accurate characterization of both spatial and temporal variability of exposure to air pollution. This work aims at proposing a method to combine the high spatial resolution of Land Use Regression (LUR) models with the high temporal resolution of fixed site monitoring data, to model spatiotemporal variability of NO₂ over a wide geographical area in Northern Italy. We developed seasonal LUR models to reconstruct the spatial distribution of a scaling factor that relates local concentrations to those measured at two reference central sites, one for the northern flat area and one for the southern mountain area. We calculated the daily average concentrations at 19 locations spread over the study areas as the product of the local scaling factor and the reference central site concentrations. We evaluated model performance comparing modeled and measured NO₂ data. LUR model's R² ranges from 0.76 to 0.92. The main predictors refers substantially to traffic, industrial land use, buildings volume and altitude a.s.l. The model's performance in reproducing measured concentrations was satisfactory. The temporal variability of concentrations was well captured: Spearman correlation between model and measures was >0.7 for almost all sites. Model's average absolute errors were in the order of 10μgm^-3. The model for the southern area tends to overestimate measured concentrations. Our modeling framework was able to reproduce spatiotemporal differences in NO₂ concentrations. This kind of model is less data-intensive than usual regional atmospheric models and it may be very helpful to assess population exposure within studies in which individual relevant exposure occurs along periods of days or months.