Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Meteorological data source comparison-a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Deciphering the impact of urban built environment density on respiratory health using a quasi-cohort analysis of 5495 non-smoking lung cancer cases

INTRODUCTION

Lung cancer is a major health concern and is influenced by air pollution, which can be affected by the density of urban built environment. The spatiotemporal impact of urban density on lung cancer incidence remains unclear, especially at the sub-city level. We aimed to determine cumulative effect of community-level density attributes of the built environment on lung cancer incidence in high-density urban areas.

METHODS

We selected 78 communities in the central city of Shanghai, China as the study site; communities included in the analysis had an averaged population density of 313 residents per hectare. Using data from the city cancer surveillance system, an age-period-cohort analysis of lung cancer incidence was performed over a five-year period (2009-2013), with a total of 5495 non-smoking/non-secondhand smoking exposure lung cancer cases. Community-level density measures included the density of road network, facilities, buildings, green spaces, and land use mixture.

RESULTS

In multivariate models, built environment density and the exposure time duration had an interactive effect on lung cancer incidence. Lung cancer incidence of birth cohorts was associated with road density and building coverage across communities, with a relative risk of 1·142 (95 % CI: 1·056-1·234, P = 0·001) and 1·090 (95 % CI: 1·053-1·128, P < 0·001) at the baseline year (2009), respectively. The relative risk increased exponentially with the exposure time duration. As for the change in lung cancer incidence over the five-year period, lung cancer incidence of birth cohorts tended to increase faster in communities with a higher road density and building coverage.

CONCLUSION

Urban planning policies that improve road network design and building layout could be important strategies to reduce lung cancer incidence in high-density urban areas.

Fetal Exposure to Air Pollution in Late Pregnancy Significantly Increases ADHD-Risk Behavior in Early Childhood

BACKGROUND

Air pollution nowadays has seriously threatened the health of the Chinese population, especially in the vulnerable groups of fetuses, infants and toddlers. In particular, the effects of air pollution on children's neurobehavioral development have attracted widespread attention. Moreover, the early detection of a sensitive period is very important for the precise intervention of the disease. However, such studies focusing on hyperactive behaviors and susceptible window identification are currently lacking in China.

OBJECTIVES

The study aims to explore the correlation between air pollution exposure and hyperactive behaviors during the early life stage and attempt to identify whether a susceptible exposure window exists that is crucial for further precise intervention.

METHODS

Based on the Longhua Child Cohort Study, we collected the basic information and hyperactivity index of 26,052 children using a questionnaire conducted from 2015 to 2017, and the Conners' Parent Rating Scale-revised (CPRS-48) was used to assess hyperactive behaviors. Moreover, the data of air pollution concentration (PM₁₀, PM_2.5, NO₂, CO, O₃ and SO₂) were collected from the monitoring station between 2011 to 2017, and a land-use random forest model was used to evaluate the exposure level of each subject. Furthermore, Distributed lag non-linear models (DLNMs) were applied for statistic analysis.

RESULTS

The risk of child hyperactivity was found to be positively associated with early life exposure to PM₁₀, PM_2.5 and NO₂. In particular, for an increase of per 10 µg/m³ in PM₁₀, PM_2.5 and NO₂ exposure concentration during early life, the risk of child hyperactivity increased significantly during the seventh month of pregnancy to the fourth month after birth, with the strongest association in the ninth month of pregnancy (PM₁₀: OR = 1.043, 95% CI: 1.016-1.071; PM_2.5: OR = 1.062, 95% CI: 1.024-1.102; NO₂: OR = 1.043, 95% CI: 1.016-1.071). However, no significant associations among early life exposure to CO, O₃ and SO₂ and child hyperactive behaviors were observed.

CONCLUSIONS

Early life exposure to PM₁₀, PM_2.5 and NO₂ is associated with an increased risk of child ADHD-like behaviors at the age around 3 years, and the late-prenatal and early postnatal periods might be the susceptible exposure windows.

Ventilation Capacities of Chinese Industrial Cities and Their Influence on the Concentration of NO2

Most cities in China, especially industrial cities, are facing severe air pollution, which affects the health of the residents and the development of cities. One of the most effective ways to alleviate air pollution is to improve the urban ventilation environment; however, few studies have focused on the relationship between them. The Frontal Area Index (FAI) can reflect the obstructive effect of buildings on wind. It is influenced by urban architectural form and is an attribute of the city itself that can be used to accurately measure the ventilation capacity or ventilation potential of the city. Here, the FAIs of 45 industrial cities of different sizes in different climatic zones in China were computed, and the relationship between the FAI and the concentration of typical pollutants, i.e., NO2, were analyzed. It was found that (1) the FAIs of most of the industrial cities in China were less than 0.45, indicating that most of the industrial cities in China have excellent and good ventilation capacities; (2) there were significant differences in the ventilation capacities of different cities, and the ventilation capacity decreased from the temperate to the tropical climate zone and increased from large to small cities; (3) there was a significant difference in the ventilation capacity in winter and summer, indicating that that with the exception of building height and building density, wind direction was also the main influencing factor of FAI; (4) the concentration of NO2 was significantly correlated with the FAI, and the relative contribution of the FAI to the NO2 concentration was stable at approximately 9% and was generally higher than other socioeconomic factors. There was a turning point in the influence of the FAI on the NO2 concentration (0.18 < FAI < 0.49), below which the FAI had a strong influence on the NO2 concentration, and above which the influence of the FAI became weaker. The results of this study can provide guidance for suppressing urban air pollution through urban planning.

Three-dimensional building morphology impacts on PM2.5 distribution in urban landscape settings in Zhejiang, China

Three-dimensional (3D) urban landscape patterns and building morphology are crucial for urban planning and essential for urban landscape functions. In this study, fixed and mobile monitoring sites were used to determine the spatial distribution of PM_2.5 concentrations in Hangzhou. Six 3D metrics were selected to analyze the response of PM_2.5 pollution to landscape patterns and building morphology, while their two-dimensional (2D) counterparts' metrics were also analyzed to contrast the differences. A variance partitioning analysis (VPA) was performed to measure the combined and relative contribution of 3D and 2D metrics to the changes in PM_2.5 concentrations. The results showed that: (1) on the 3D scale, forming a building pattern with a combination of different building heights can eliminate the accumulation of PM_2.5; (2) on the 2D scale, fragmentation and decentralization of landscapes and building patches alleviate PM_2.5 pollution; and (3) 3D building morphology indicators have the highest explanatory power (40.94%) for the changes of PM_2.5 concentrations. It turns out that the explanatory power of 3D metrics for PM_2.5 concentrations changes is much greater than that of 2D metrics. In addition, when compared to building morphology indicators from a single dimension, the combination of 2D and 3D metrics is better able to reflect urban PM_2.5 pollution. The results of this study expand our understanding of how PM_2.5 pollution responds to 2D and 3D metrics and provide useful information for urban planning.

Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method

A non-parametric ensemble model was proposed to estimate the long-term (2015-2019) particle surface area concentrations (S_A) over China for the first time on basis of a vilification dataset of measured particle number size distribution. This ensemble model showed excellent cross-validation R² value (CV R² = 0.83) as well as a relatively low root-mean-square error (RMSE = 195.0 μm²/cm³). No matter in which year, considerable spatial heterogeneity of S_A was found over China with higher S_A in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Middle Lower Reaches of Yangtze River (MLYR). From 2015 to 2019, S_A significantly decreased in representative city clusters. The reduction rates were 140.1 μm²·cm^-3·a^-1 in BTH, 110.7 μm²·cm^-3·a^-1 in Pearl River Delta (PRD), 105.2 μm²·cm^-3·a^-1 in YRD, and 92.4 μm²·cm^-3·a^-1 in Sichuan Basin (SCB), respectively. Even though such quick reduction, high S_A (ranged from ~800 μm²/cm³ to ~1750 μm²/cm³) during the heavy pollution period (PM_2.5 > 75 μg/m³) still existed in the above-mentioned city clusters and may provide rich reaction vessels for multiphase chemistry. A dichotomy of enhanced annual 4th maximum daily 8-h average O₃ concentrations (4MDA8 O₃) and decreased S_A during summertime was found in Shanghai, a representative city of YRD. In Chengdu (SCB), increased 4MDA8 O₃ concentration was associated with a synchronous increase of S_A from 2017 to 2019. Differently, 4MDA8 O₃ concentrations enhanced in Beijing (BTH) and Guangzhou (PRD), while not significant for S_A before 2018. This work will greatly deepen our understanding of the historical variation and spatial distributions of S_A over China.

A novel assessment framework for improving air quality monitoring network layout

Redundant stations in the air quality monitoring network (AQMN), not only cause high maintenance and operation costs, but also affect the performance of air quality assessment. This study presents a novel framework for identifying the redundant stations and selecting the corresponding alternatives in AQMN. The framework composes three main steps. Firstly, we identify the redundant stations by correlation analysis and stepwise regression methods. Secondly, we determine the corresponding alternative stations by cluster analysis and correspondence analysis methods. Finally, the final optimization results are verified by the support vector regression. We perform empirical evaluations of the framework using Shanghai's AQMN. The results show that Xuhui, Zhangjiang, Shiwuchang, and Pudong New Area are four redundant pollution monitoring stations. Alternatives for each type of pollutant for these redundant stations are proposed and the adjusted layout of AQMN is verified with historical data. The framework proposed in this study can effectively improve the layout of AQMN, which could be applied to other cities or regions to improve the integrity of pollution information and reduce the monitoring costs.Implications: In this study, we set up a comprehensive framework. A case study proves that the framework we proposed can help countries identify redundant stations, so as to reduce the monitoring costs, improve the monitoring efficiency, and provide technical support for governments to implement accurate air quality control measures.Four particularly important aspects were highlighted in this work: (i) A new framework was constructed that combined regression and prediction for the first time to analyze and validate pollutant data; (ii) The framework used Stepwise Regression to improve previous methods for identifying redundant monitoring stations, effectively improving identification efficiency; (iii) The framework used Support Vector Regression to make predictions to verify the final results of the optimized layout, which was ignored in previous studies. (iv) This framework can be applied to any city or region, which has important practical significance for improving the comprehensiveness and accuracy of pollution monitoring in various cities.

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.

Long-term association between urban air ventilation and mortality in Hong Kong

While associations between population health outcomes and some urban design characteristics, such as green space, urban heat islands (UHI), and walkability, have been well studied, no prior studies have examined the association of urban air ventilation and health outcomes. This study used data from Hong Kong, a densely populated city, to explore the association between urban air ventilation and mortality during 2008-2014. Frontal area density (FAD), was used to measure urban ventilation, with higher FAD indicating poorer ventilation, due to structures blocking wind penetration. Negative binomial regression models were constructed to regress mortality counts for each 5-year age group, gender, and small area group, on small area level variables including green space density, population density and socioeconomic indicators. An interquartile range increase in FAD was significantly associated with a 10% (95% confidence interval (CI) 2%-19%, p = 0.019) increase in all-cause mortality and a 21% (95% CI: 2%-45%, p = 0.030) increase in asthma mortality, and non-significantly associated with a 9% (95% CI: 1%-19%, p = 0.073) in cardio-respiratory mortality. Better urban ventilation can help disperse vehicle-related pollutants and allow moderation of UHIs, and for a coastal city may allow moderation of cold temperatures. Urban planning should take ventilation into account. Further studies on urban ventilation and health outcomes from different settings are needed.

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.

Monitoring, Mapping, and Modeling Spatial-Temporal Patterns of PM2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies

Fine particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is highly variable in space and time. In this study, the dynamics of PM2.5 concentrations were mapped at high spatio-temporal resolutions using bicycle-based, mobile measures on a university campus. Significant diurnal and daily variations were revealed over the two-week survey, with the PM2.5 concentration peaking during the evening rush hours. A range of predictor variables that have been proven useful in estimating the pollution level was derived from Geographic Information System, high-resolution airborne images, and Light Detection and Ranging (LiDAR) datasets. Considering the complex interplay among landscape, wind, and air pollution, variables influencing the PM2.5 dynamics were quantified under a new wind wedge-based system that incorporates wind effects. Panel data analysis models identified eight natural and built environment variables as the most significant determinants of local-scale air quality (including four meteorological factors, distance to major roads, vegetation footprint, and building and vegetation height). The higher significance level of variables calculated using the wind wedge system as compared to the conventional circular buffer highlights the importance of incorporating the relative position of emission sources and receptors in modeling.

A hybrid kriging/land-use regression model with Asian culture-specific sources to assess NO2 spatial-temporal variations

Kriging interpolation and land use regression (LUR) have characterized the spatial variability of long-term nitrogen dioxide (NO₂), but there has been little research on combining these two methods to capture small-scale spatial variation. Furthermore, studies predicting NO₂ exposure are almost exclusively based on traffic-related variables, which may not be transferable to Taiwan, a typical Asian country with diverse local emission sources, where densely distributed temples and restaurants may be important for NO₂ levels. To advance the exposure estimates in Taiwan, a hybrid kriging/LUR model incorporates culture-specific sources as potential predictors. Based on 14-year NO₂ observations from 73 monitoring stations across Taiwan, a set of interpolated NO₂ values were generated through a leave-one-out ordinary kriging algorithm, and this was included as an explanatory variable in the stepwise LUR procedures. Kriging interpolated NO₂ and culture-specific predictors were entered in the final models, which captured 90% and 87% of NO₂ variation in annual and monthly resolution, respectively. Results from 10-fold cross-validation and external data verification demonstrate robust performance of the developed models. This study demonstrates the value of incorporating the kriging-interpolated estimates and culture-specific emission sources into the traditional LUR model structure for predicting NO₂, which can be particularly useful for Asian countries.

The high-resolution estimation of sulfur dioxide (SO2) concentration, health effect and monetary costs in Beijing

Severe air pollution episodes with high SO₂ loading have been frequently observed during the last decades in Beijing and have caused a noticeable damage to human health. To advance the spatiotemporal prediction of SO₂ exposure in Beijing, we developed the monthly land use regression (LUR) models using daily SO₂ concentration data collected from 34 monitoring stations during 2016 and 7 categories of potential independent variables (socio-economic factors, traffic and transport, emission source, land use, meteorological data, building morphology and Geographic location) in Beijing. The average adjusted R² of 12 final LUR models was 0.62, and the root-mean-squared error (RMSE) was 4.12 μg/m³. The LOOCV R² and RMSE of LUR models reached 0.56 and 5.43 μg/m³, respectively, suggesting that the LUR models achieved the satisfactory performance. The prediction results suggested that the average SO₂ level in Beijing was 11.06 μg/m³ with the highest one up to 22.49 μg/m³ but the lowest one down to 3.86 μg/m³. The SO₂ exposure showed strong spatial heterogeneity, which was much higher in the southern area than that in the northern in Beijing. The mortality and morbidity due to the excessive SO₂ concentration were estimated to be 73 (95% CI:(38-125)) and 27854 (95% CI:(13852-41659)) cases per year in Beijing, leading to economic cost of 35.76 (95% CI:(16.45-54.06)) and 441.47 (95% CI:(318.31-562.04)) million RMB Yuan in 2016, respectively. This study clarified the intra- and inter-regional transport modeling of the SO₂ pollution in Beijing and supplied an important support for the future air-quality and public health management strategies.

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.

A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China

BACKGROUND

Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings.

OBJECTIVE

A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings.

METHODS

In each of four seasons in 2016-2017, NO₂ was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM_2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO₂ and PM_2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models.

RESULTS

We developed robust LUR models at the ground level for estimated annual averages of NO₂ (R²: 0.71, adjusted R²: 0.67, and Leave-One-Out Cross Validation (LOOCV) R²: 0.64) and PM_2.5 (R²: 0.77, adjusted R²: of 0.73, and LOOCV R²: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO₂ showed similar patterns. Vertical variation of NO₂ and PM_2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations.

CONCLUSIONS

Ground-level NO₂ and PM_2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration

This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O₃ concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency's (EPA) data of O₃ concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R² of 0.74 and a 10-fold cross-validated R² of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O₃ concentration variation in Asian areas.

Assessing spatial variability of extreme hot weather conditions in Hong Kong: A land use regression approach

The number of extreme hot weather events have considerably increased in Hong Kong in the recent decades. The complex urban context of Hong Kong leads to a significant intra-urban spatial variability in climate. Under such circumstance, a spatial understanding of extreme hot weather condition is urgently needed for heat risk prevention and public health actions. In this study, the extreme hot weather events of Hong Kong were quantified and measured using two indicators - very hot day hours (VHDHs) and hot night hours (HNHs) which were counted based on the summertime hourly-resolved air temperature data from a total of 40 weather stations (WSs) from 2011 to 2015. Using the VHDHs and HNHs at the locations of the 40 WSs as the outcome variables, land use regression (LUR) models are developed to achieve a spatial understanding of the extreme hot weather conditions in Hong Kong. Land surface morphology was quantified as the predictor variables in LUR modelling. A total of 167 predictor variables were considered in the model development process based on a stepwise multiple linear regression (MLR). The performance of resultant LUR models was evaluated via cross validation. VHDHs and HNHs were mapped at the community level for Hong Kong. The mapping results illustrate a significant spatial variation in the extreme hot weather conditions of Hong Kong in both the daytime and nighttime, which indicates that the spatial variation of land use configurations must be considered in the risk assessment and corresponding public health management associated with the extreme hot weather.

Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5

Fine particulate matter (PM_2.5) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM_2.5, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM_2.5, especially in areas with high spatiotemporal variability of PM_2.5. In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation. In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity

Urban heat island (UHI) effect significantly raises the health burden and building energy consumption in the high-density urban environment of Hong Kong. A better understanding of the spatiotemporal pattern of UHI is essential to health risk assessments and energy consumption management but challenging in a high-density environment due to the sparsely distributed meteorological stations and the highly diverse urban features. In this study, we modelled the spatiotemporal pattern of UHI effect using the land use regression (LUR) approach in geographic information system with meteorological records of the recent 4years (2013-2016), sounding data and geographic predictors in Hong Kong. A total of 224 predictor variables were calculated and involved in model development. As a result, a total of 10 models were developed (daytime and nighttime, four seasons and annual average). As expected, meteorological records (CLD, Spd, MSLP) and sounding indices (KINX, CAPV and SHOW) are temporally correlated with UHI at high significance levels. On the top of the resultant LUR models, the influential spatial predictors of UHI with regression coefficients and their critical buffer width were also identified for the high-density urban scenario of Hong Kong. The study results indicate that the spatial pattern of UHI is largely determined by the LU/LC (RES1500, FVC500) and urban geomorphometry (h¯, BVD, λ¯_F, Ψ_sky and z₀) in a high-density built environment, especially during nighttime. The resultant models could be adopted to enrich the current urban design guideline and help with the UHI mitigation.

Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

Particulate matters (PM) at the pedestrian level significantly raises the health impacts in the compact urban environment of Hong Kong. A detailed investigation of the fine-scale spatial variation of pedestrian-level PM is necessary to assess the health risk to pedestrians in the outdoor environment. However, the collection of PM data is difficult in the compact urban environment of Hong Kong due to the limited amount of roadside monitoring stations and the complicated urban context. In this study, we measured the fine-scale spatial variability of the PM in three of the most representative commercial districts of Hong Kong using a backpack outdoor environmental measuring unit. Based on the measurement data, 13 types of geospatial interpolation methods were examined for the spatial mapping of PM_2.5 and PM₁₀ with a group of building geometrical covariates. Geostatistical modelling was adopted as the basis of spatial interpolation of the PM. The results show that the original cokriging with the exponential kernel function provides the best performance in the PM mapping. Using the fine-scale building geometrical features as covariates slightly improves the interpolation performance. The study results also imply that the fine-scale, localized pollution emission sources heavily influence pedestrian exposure to PM.