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Distribution patterns and influencing factors of population exposure risk to particulate matters based on cell phone signaling data
... For example, Liu et al. (2020) scrutinized the association between urban land use and commuting flows. Zhang et al. (2023) assessed population exposure risk to particulate matter. Zhang et al. (2020) analyzed the megaregional structure in the Pearl River Delta, China. ...
Urban vitality has long been a focus of urban planners and designers. This study portrays the spatiotemporal patterns of urban vitality in Chengdu, China, on weekdays and weekends and examines their relationship with a set of factors using spatial regression and geographically weighted regression models. The results reveal that the spatial distribution of urban vitality displays a clustered and polycentric pattern, which can be attributed to urban spatial structure. Urban vitality varies considerably throughout the day on weekdays and weekends, and it consistently decreases as the distance to the city center increases. Furthermore, weekday vitality is much more predictable (less random) than weekend vitality, indicating that human activity patterns present higher regularity on weekdays than on weekends. The normalized difference vegetation index (NDVI), road density, the number of residential facilities, and land-use mix have positive influences on urban vitality on weekdays and weekends, while the distance to the metro station has negative influences. The influences of NDVI on urban vitality in the city center are negative on weekdays but become positive on weekends. The influences of the distance to the metro station are more homogeneously distributed on weekends than on weekdays. Finally, this study calls for temporally and spatially varying policies for urban vitality enhancement.
Urban heat islands (UHIs) exert a substantially negative impact on human health and urban sustainability. The role of two-dimensional (2D) landscape patterns in UHIs are well documented; while the effects and contributions of three-dimensional (3D) urban structures remain unclear, especially across different climatic zones. Here we investigated the relationship between 2D/3D urban morphology and the urban thermal environment in summer and winter during the day and at night in 62 representative large cities across four major climate zones in China. First, we extracted the seasonal surface regional heat island intensity (SRHII) using the MODIS 8-Day land surface temperature product. Subsequently, we constructed 25 2D and five 3D urban features and explored their relative importance and respective roles in UHIs in different climatic contexts. Results show that: (1) significant differences (p < 0.05) exist in SRHII between various climate zones; cities with a humid subtropical climate experience temperatures approximately 2 °C higher during the day in summer compared to those with the other climate types. (2) 3D urban features can effectively improve the interpretation of urban features for SRHII, with an average optimization level of 21%. (3) Urban trees have a higher cooling effect than other green spaces, whereas tall buildings can also reduce the UHI effect. (4) On summer days, equal proportions of tree to building volume provide the greatest cooling effects. This study provides new insights into the effect of 3D urban characteristics on SRHII and has promising implications for climate resilience planning and heat-related risk management
Background
Few mobility-based studies have investigated the associations between multiple environmental exposures, including social exposures, and mental health.
Objective
To assess how exposure to green space, blue space, noise, air pollution, and crowdedness along people's daily mobility paths are associated with anxiety symptoms.
Methods
358 participants were cross-sectionally tracked with Global Positioning System (GPS)-enabled mobile phones. Anxiety symptoms were measured at baseline using the Generalized Anxiety Disorder-7 (GAD-7) questionnaire. Green space, blue space, noise, and air pollution were assessed based on concentric buffers of 50 m and 100 m around each GPS point. Crowdedness was measured by the number of nearby Bluetooth-enabled devices detected along the mobility paths. Multiple linear regressions with full covariate adjustment were fitted to examine anxiety-environmental exposures associations. Random forest models were applied to explore possible nonlinear associations and exposure interactions.
Results
Regression results showed null linear associations between GAD-7 scores and environmental exposures. RF models indicated that GAD-7-environment associations varied nonlinearly with exposure levels. We found a negative association between green space and GAD-7 scores only for participants with moderate green space exposure. We observed a positive association between GAD-7 scores and noise levels above 60 dB and air pollution concentrations above 17.2 μg m⁻³. Crowdedness was positively associated with GAD-7 scores, but exposure-response functions flattened out with pronounced crowdedness of >7.5. Blue space tended to be positively associated with GAD-7 scores. RF models ranked environmental exposures as more important to explain GAD-7 scores than linear models.
Conclusions
Our findings indicate possible nonlinear associations between mobility-based environmental exposures and anxiety symptoms. More studies are needed to obtain an in-depth understanding of underlying anxiety-environment mechanisms during daily life.
In this article, we present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. An hourly population distribution dataset is provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The dataset is validated by comparing population register data from Statistics Finland for night hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city, and examine population variations relevant to spatial accessibility analyses, crisis management, planning and beyond.
In order to formulate policies to control regional air pollution and promote sustainable human–land system development, it is crucial to study the space–time distribution of air pollution and the population exposure risk. Existing studies are limited to individual fine particulate pollutants, which does not fully reflect the comprehensiveness of air quality. In addition, the spatiotemporal distribution of air quality and population exposure risk at different scales need to be further quantified. In this study, we used air monitoring station data and population spatial distribution data to analyze the spatiotemporal characteristics of air quality, including seasonal variations, variations before and during heating periods, and the occurrence frequency of priority pollutants in the traditional industrial areas of Northeast China in 2015. The population exposure–air pollution risk (PE-APR) model was used to calculate the population exposure risk at different spatial scales. The results suggest that GIS methods and air monitoring data help to establish a comprehensive air quality analysis framework, revealing spring–summer differentiation and the change trend of air quality with latitude. There are significant clustering features of air quality. A grid-scale population exposure–air pollution risk map is not restricted by administrative boundaries, which helps to discover high-risk areas of the main regional economic corridors and differences between inner cities and suburbs. This study provides a reference for understanding the space–time evolution of regional air pollution and formulating coordinated cross-regional air pollution strategies.
Air pollution is currently becoming a significant global environmental issue. The sources of air pollution in Malaysia are mobile or stationary. Motor vehicles are one of the mobile sources. Stationary sources originated from emissions caused by urban development, quarrying and power plants and petrochemical. The most noticeable contaminant in the Peninsular of Malaysia is the particulate matter (PM 10 ), the highest contributor of Air Pollution Index (API) compared to other pollution parameters. The aim of this study is to determine the best loss function between quantile regression (QR) and ordinary least squares (OLS) using boosted regression tree (BRT) for the prediction of PM 10 concentration in Alor Setar, Klang and Kota Bharu, Malaysia. Model comparison statistics using coefficient of determination (R ² ), prediction accuracy (PA), index of agreement (IA), normalized absolute error (NAE) and root mean square error (RMSE) show that QR is slightly better than OLS with the performance of R ² (0.60–0.73), PA (0.78–0.85), IA (0.86–0.92), NAE (0.15–0.17) and RMSE (9.52–22.15) for next-day predictions in BRT model.
Background
Exposure to air pollution and physical inactivity are both significant risk factors for non-communicable diseases (NCDs). These risk factors are also linked so that the change in exposure in one will impact risks and benefits of the other. These links are well captured in the active transport (walking, cycling) health impact models, in which the increases in active transport leading to increased inhaled dose of air pollution. However, these links are more complex and go beyond the active transport research field. Hence, in this study, we aimed to summarize the empirical evidence on the links between air pollution and physical activity, and their combined effect on individual and population health.
Objectives and methods
We conducted a non-systematic mapping review of empirical and modelling evidence of the possible links between exposure to air pollution and physical activity published until Autumn 2019. We reviewed empirical evidence for the (i) impact of exposure to air pollution on physical activity behaviour, (ii) exposure to air pollution while engaged in physical activity and (iii) the short-term and (iv) long-term health effects of air pollution exposure on people engaged in physical activity. In addition, we reviewed (v) public health modelling studies that have quantified the combined effect of air pollution and physical activity. These broad research areas were identified through expert discussions, including two public events performed in health-related conferences.
Results and discussion
The current literature suggests that air pollution may decrease physical activity levels during high air pollution episodes or may prevent people from engaging in physical activity overall in highly polluted environments. Several studies have estimated fine particulate matter (PM2.5) exposure in active transport environment in Europe and North-America, but the concentration in other regions, places for physical activity and for other air pollutants are poorly understood. Observational epidemiological studies provide some evidence for a possible interaction between air pollution and physical activity for acute health outcomes, while results for long-term effects are mixed with several studies suggesting small diminishing health gains from physical activity due to exposure to air pollution for long-term outcomes. Public health modelling studies have estimated that in most situations benefits of physical activity outweigh the risks of air pollution, at least in the active transport environment. However, overall evidence on all examined links is weak for low- and middle-income countries, for sensitive subpopulations (children, elderly, pregnant women, people with pre-existing conditions), and for indoor air pollution.
Conclusions
Physical activity and air pollution are linked through multiple mechanisms, and these relations could have important implications for public health, especially in locations with high air pollution concentrations. Overall, this review calls for international collaboration between air pollution and physical activity research fields to strengthen the evidence base on the links between both and on how policy options could potentially reduce risks and maximise health benefits.
Notifications are one of the core functionalities of smartphones. Previous research suggests they can be a major disruption to the professional and private lives of users. This paper presents evidence from a mixed-methods study using first-person wearable video cameras, comprising 200 h of audio-visual first-person, and self-confrontation interview footage with 1130 unique smartphone interactions (N = 37 users), to situate and analyse the disruptiveness of notifications in real-world contexts. We show how smartphone interactions are driven by a complex set of routines and habits users develop over time. We furthermore observe that while the duration of interactions varies, the intervals between interactions remain largely invariant across different activity and location contexts, and for being alone or in the company of others. Importantly, we find that 89% of smartphone interactions are initiated by users, not by notifications. Overall this suggests that the disruptiveness of smartphones is rooted within learned user behaviours, not devices.
Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou’s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers.
Air pollution is one of the most concerning environmental problems in cities. Hourly data on pollutant concentrations from 11 automatic atmospheric monitoring stations and meteorological data in Shenyang from 2017 to 2019 were used to analyze the spatio-temporal variation rules of CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), O3 (ozone), PM2.5 and PM10 (PM particles with an aerodynamic diameters of not more than 2.5 µm and 10 µm) and their relationships with meteorological parameters. Meanwhile, the regional transmission route of pollutants was analyzed by the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model. The results showed that the concentration of O3 in the northern area of the city was higher than that in the south; CO, SO2 and NO2 were relatively high in the urban center; and PM2.5 and PM10 were relatively high in the southwest. The average concentration of pollutants was lowest in 2019. The concentration of O3 was the highest in spring, while CO showed no significant variations between different seasons. The remaining pollutant concentrations appeared to be high in winter and low in summer. The cumulative concentrations of the six pollutants were the highest in March, and relatively low in July–September. The diurnal concentration variations of O3, CO and SO2 exhibited a “single peak,” while others showed a “double peak and double valley.” Temperature was positively correlated with O3 concentration and negatively correlated with others. Wind speed was negatively correlated with the concentration of PM2.5, NO2, and O3. The air quality of the main urban area in spring and summer was mainly affected by the coastal air flow, while it was mostly affected by the northwest air flow in autumn and winter.
In this study, we propose a spatial analytical framework to better understand tourist experiences from geotagged social media data in Beijing in 2013. Based on text analytics, deep learning classifiers, and econometric analysis, we investigated the effects of air pollution on tourists' experiences in terms of their behavioral, emotional, and health outcomes. Results indicate that a higher PM2.5 concentration led to a broader travel scope within Beijing with activities closer to the city center. Tourists reported fewer positive sentiments and more health issues due to increasing air pollution. Further, a comparison of residents and tourists revealed differential pollution sensitivity and adaptation strategies. We also developed a Web-GIS–based platform integrating various models to enable tourism planners to design better tourism experiences.
Air quality issue such as particulate matter pollution (PM2.5 and PM10) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of particles and analyze the causality factors. The results demonstrated that variation trend of particles was almost similar, the annual, monthly and daily distribution had their own characteristics. Particles decreased gradually from south to north, from west to east. Correlation analysis showed that wind speed was the most important factor affecting particles, and temperature, air pressure and relative humidity were key factors in some seasons. Path analysis showed that there was complex unidirectional causal relationship between particles and individual or combined effects, and NO2 and CO were key factors affecting PM2.5. The hot and cold areas changed little with the seasons. All the above results suggests that planning the industrial layout, adjusting industrial structure, joint prevention and control were necessary measure to reduce particles concentration.
One of our era's greatest scourges is air pollution, on account not only of its impact on climate change but also its impact on public and individual health due to increasing morbidity and mortality. There are many pollutants that are major factors in disease in humans. Among them, Particulate Matter (PM), particles of variable but very small diameter, penetrate the respiratory system via inhalation, causing respiratory and cardiovascular diseases, reproductive and central nervous system dysfunctions, and cancer. Despite the fact that ozone in the stratosphere plays a protective role against ultraviolet irradiation, it is harmful when in high concentration at ground level, also affecting the respiratory and cardiovascular system. Furthermore, nitrogen oxide, sulfur dioxide, Volatile Organic Compounds (VOCs), dioxins, and polycyclic aromatic hydrocarbons (PAHs) are all considered air pollutants that are harmful to humans. Carbon monoxide can even provoke direct poisoning when breathed in at high levels. Heavy metals such as lead, when absorbed into the human body, can lead to direct poisoning or chronic intoxication, depending on exposure. Diseases occurring from the aforementioned substances include principally respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD), asthma, bronchiolitis, and also lung cancer, cardiovascular events, central nervous system dysfunctions, and cutaneous diseases. Last but not least, climate change resulting from environmental pollution affects the geographical distribution of many infectious diseases, as do natural disasters. The only way to tackle this problem is through public awareness coupled with a multidisciplinary approach by scientific experts; national and international organizations must address the emergence of this threat and propose sustainable solutions.
China has made great efforts towards air pollutant concentration control during the past five years, which has led to positive outcomes. However, air pollutant concentration focused efforts were considered separately from human exposure risk. And this might result in a misunderstanding that reducing exposure risk can only rely on the national level measures of air pollutant control. This study integrates the first Chinese survey of human activity patterns and the spatially continuous high-resolution PM 2.5 concentration maps to reveal the spatial and temporal variations of China's air pollution exposure risk from 2013 to 2017. More importantly, the effects on risk reduction from multi-scale and multi-object perspectives (reductions of ambient PM 2.5 concentrations by national or provincial measures and changes of individual behavior patterns by personal efforts) are deeply investigated. Results show that the reductions of PM 2.5 concentration and associated reductions of exposure risk from 2013 to 2017 were 40% and 35.7%, respectively. They also showed that both the reduction of PM 2.5 concentrations and change of personal behavior patterns were effective for risk reduction when China's total PM 2.5 exposure risk was higher than 1.58. However, only individual behavior changes contributed to risk reduction for scenarios with state-level risk value below 1.58. For regional strategies, threshold values for PM 2.5 exposure risk control differentiating national measures or personal efforts were spatially and temporally dependent. The role of personal behavior changes on PM 2.5 exposure risk reduction was growing in these five years with concentration rapidly decreasing regions. The findings suggest that people-centered air pollution exposure risk prevention not only depends on government management for air pollution control, but also on individual changes of activity patterns. Efforts from the state and individuals are both essential for reducing air pollution exposure risk in China, especially growing individual efforts are needed in regions with the decreasing air pollutant concentrations in the coming future. Moreover, this study mainly discussed the PM 2.5 exposure risk from the macroscopic perspective, the research at the microcosmic perspective is also needed in the further study.
Air pollution is a major environmental health problem. The study of interaction between air pollution and human will benefit to the human health and well-being of community. Both a model for assessing population relative risk of air pollution exposure (MAPRRAPE) and air pollution concentration methods were applied in a case study to determine the optimal method in evaluating risk of population exposure to Sulfur Dioxide (SO2). The framework for building the MAPRRAPE was described in detail. Then, the spatial patterns of population by demographic characteristics exposed to SO2 from industrial, vehicle, and the mixture of industrial and vehicle pollution sources, as well as an in-depth quantitative investigation using correlation analysis were studied for further source appointment. The results showed that the MAPRRAPE was more reliable than air pollution concentration model in determining population exposure risks by demographic characteristics. The high risk areas of whites exposed to SO2 were larger than blacks and the other races due to a large number of whites, and other age groups exposed to SO2 were larger than children and the old people. In addition, the correlation analyses showed that the relative risks of population by demographic characteristics exposed to SO2 had a more significant correlation with vehicle pollution source than industrial pollution source. The results of source appointment thus demonstrated that vehicle pollution source was the main pollution source. This study suggests that there is a clear need for the implementation of programs and services that will reduce population exposed to air pollution with focusing on densely populated areas for an ultimate improvement of community health status and the environmental conditions.
Respiratory disease admission has been increasing in the recent 5 years due to heavy air pollutions and bad weather conditions in China. We investigated the short-term association of ambient air pollution with daily counts of hospital admissions due to respiratory infection diseases with stratified analysis by age (0–18, 19–65, and > 65 years old), gender (male, female), season (spring, summer, autumn, winter), and disease type (lung infections, asthma, COPD (chronic obstructive disease), URI (upper respiratory infections)) in heavy polluted city of Shenyang in China. Daily ambient air pollution concentrations, weather conditions, and hospital admission counts for 53 months (from November 1, 2013 to March 25, 2018) were extracted from related authorities in electronic databases. Associations between outdoor air pollution levels and hospital admissions were estimated for time lags of 0–7 days using quasi-Poisson additive regression models, adjusted for meteorological variables, holidays, day of week, and season, as well as eliminating autocorrelations. Single pollutant analysis results showed lung infection diseases were related to all pollutant concentration change with no lag effects. After adjusting for other pollutants and confounding factors, we found NO2 was associated with daily admissions of lung infections (ER = 6.75%, 95% CI 1.24, 12.55), asthma (ER = 20.36%, 95% CI 4.26, 38.95; lag day 5, ER = 18.48%, 95% CI 2.83, 36.51), and COPD (ER = 13.27%, 95% CI 0.46, 27.71); CO was associated with lung infections and asthma with lag effects on lag days 1 and 4; and PM2.5 was associated with COPD admissions on lag day 6. Respiratory hospital admissions in female over 65 years old and autumn were more associated with increased air pollutant levels. Our study results might add more detail evidences for relationship studies between air pollution exposure and respiratory diseases and contribute to the precise respiratory disease prevention and air pollution control strategies.
From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM 2.5 ) concentrations occurred nationwide. Here we estimate the drivers of the improved PM 2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population–weighted annual mean PM 2.5 concentrations decreased from 61.8 (95%CI: 53.3–70.0) to 42.0 µg/m ³ (95% CI: 35.7–48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM 2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM 2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9–7.1), 4.4- (95% CI: 3.8–4.9), 2.8- (95% CI: 2.5–3.0), and 2.2- (95% CI: 2.0–2.5) µg/m ³ declines in the national PM 2.5 concentration in 2017, respectively, and further reduced PM 2.5 -attributable excess deaths by 0.37 million (95% CI: 0.35–0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China’s recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.
The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.
Using real-time data from portable air pollutant sensors and smartphone Global Positioning System trajectories collected in Beijing, China, this study demonstrates how smart technologies and individual activity-travel microenvironments affect the assessment of individual-level pollution exposure in space and time at a very fine resolution. It compares three different types of individual-level exposure estimates generated by using residence-based monitoring station assessment, mobility-based monitoring station assessment, and mobility-based real-time assessment. Further, it examines the differences in personal exposure to PM2.5 associated with different activity places and travel modes across various environmental conditions. The results show that the exposure estimates generated by monitoring station assessment and real-time sensing assessment vary substantially across different activity locations and travel modes. Individual-level daily exposure for residents living in the same community also varies significantly, and there are substantial differences in exposure levels using different approaches. These results indicate that residence- or mobility-based monitoring station assessments, which cannot account for the differences in air pollutant exposures between outdoor and indoor environments and between different travel-related microenvironments, could generate considerably biased estimates of personal pollution exposure. Key Words: indoor environment, real-time exposure to air pollution, smart technologies, travel modes, the uncertain geographic context problem.
Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models - linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms - which resulted in cross-validation R2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.
In an increasingly urbanised world where mental health is currently in crisis, interventions to increase human engagement and connection with the natural environment are one of the fastest growing, most widely accessible, and cost-effective ways of improving human wellbeing. This study aimed to provide an evaluation of a smartphone app-based wellbeing intervention. In a randomised controlled trial study design, the app prompted 582 adults, including a subgroup of adults classified by baseline scores on the Recovering Quality of Life scale as having a common mental health problem (n = 148), to notice the good things about urban nature (intervention condition) or built spaces (active control). There were statistically significant and sustained improvements in wellbeing at one-month follow-up. Importantly, in the noticing urban nature condition, compared to a built space control, improvements in quality of life reached statistical significance for all adults and clinical significance for those classified as having a mental health difficulty. This improvement in wellbeing was partly explained by significant increases in nature connectedness and positive affect. This study provides the first controlled experimental evidence that noticing the good things about urban nature has strong clinical potential as a wellbeing intervention and social prescription.
Few researches have been investigated on the effects of ambient air pollutants from coal combustion on acute exacerbation of chronic obstructive pulmonary disease (AECOPD) hospitalizations. The whole time series was split into heating season and non-heating season. We used a quasi-Poisson generalized linear regression model combined with distributed lag non-linear models (DLNMs) to estimate the relative cumulative risk and calculate the air pollutant hospitalization burden of AECOPD for lag 0–7 days in heating season and non-heating season. There were higher PM2.5, PM10, NO2, SO2, and CO concentrations in heating seasons than non-heating season in Shijiazhuang; however, O3 was higher in non-heating season than heating season. The AECOPD-associated relative cumulative risks for PM2.5, PM10, NO2, and SO2 for lag 0–7 days were significantly positively associated with hospitalization in heating and non-heating season; we found that the cumulative relative risk of NO2 was the greatest in every 1 unit of air pollutants during the heating season and the cumulative relative risk of SO2 was the greatest during the non-heating season. The results showed that 17.8%, 12.9%, 1.7%, 16.7%, and 10.5% of AECOPD hospitalizations could be attributable to PM2.5, PM10, SO2, NO2, and CO exposure in heating season, respectively. However, the results showed that 19.5%, 22.4%, 15%, 8.3%, and 10.4% of AECOPD hospitalizations could be attributable to PM2.5, PM10, SO2, NO2, and O3 exposure in non-heating season, respectively. The attributable burden of AECOPD hospitalization in heating season and non-heating season are different. PM2.5, PM10, NO2, and CO are the main factors of heating season, while PM10, PM2.5, SO2, and O3 are the main factors of non-heating season. In conclusions, the centralized heating can change the influence of attributable risk. When government departments formulate interventions to reduce the risk of acute hospitalization of chronic obstructive pulmonary disease (COPD), the influence of heating on disease burden should be considered.
In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM2.5 concentration greater than 80 μg/m3 in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.
Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.
Significance
Severe haze events with large temporal/spatial coverages have occurred frequently in wintertime northern China. These extremes result from a complex interplay between emissions and atmospheric processes and provide a unique scientific platform to gain insights into many aspects of the relevant atmospheric chemistry and physics. Here we synthesize recent progress in understanding severe haze formation in northern China. In particular, we highlight that improved understanding of the emission sources, physical/chemical processes during haze evolution, and interactions with meteorological/climatic changes are necessary to unravel the causes, mechanisms, and trends for haze pollution. This viewpoint established on the basis of sound science is critical for improving haze prediction/forecast, formulating effective regulatory policies by decision makers, and raising public awareness of environmental protection.
Satellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset.
Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents' stay and commuters' travel exposure to outdoor PM2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents' stay activities in each analysis zone, and then focus on commuters and estimate their travel routes with a traffic assignment model. Based on air quality observations from monitoring stations and a spatial interpolation model, we estimate the outdoor PM2.5 concentrations at a 500-m grid level and map them to road networks. We then estimate the travel exposure for each road segment by multiplying the PM2.5 concentration and travel time spent on the road. By combining the estimated PM2.5 exposure and housing price harnessed from online housing transaction platforms, we discover that in the winter, Beijing commuters with low wealth level are exposed to 13% more PM2.5 per hour than those with high wealth level when staying at home, but exposed to less PM2.5 by 5% when commuting the same distance (due to lighter traffic congestion in suburban areas). We also find that the residents from the southern suburbs of Beijing have both lower level of wealth and higher stay- and travel- exposure to PM2.5, especially in the winter. These findings inform more equitable environmental mitigation policies for future sustainable development in Beijing. Finally, or the first time in the literature, we compare the results of exposure estimated from passive data with subjective measures of perceived air quality (PAQ) from a survey. The PAQ data was collected via a mobile-app. The comparison confirms consistencies in results and the advantages of the big data for air pollution exposure assessments.
This research examines whether individual exposures to traffic congestion are significantly different between assessments obtained with and without considering individuals’ activity-travel patterns in addition to commuting trips. We used crowdsourced real-time traffic congestion data and the activity-travel data of 250 individuals in Los Angeles to compare these two assessments of individual exposures to traffic congestion. The results revealed that individual exposures to traffic congestion are significantly underestimated when their activity-travel patterns are ignored, which has been postulated as a manifestation of the uncertain geographic context problem (UGCoP). The results also highlighted that the probability distribution function of exposures is heavily skewed but tends to converge to its average when individuals’ activity-travel patterns are considered when compared to one obtained when those patterns are not considered, which indicates the existence of the neighborhood effect averaging problem (NEAP). Lastly, space-time visualizations of individual exposures illustrated that people’s exposures to traffic congestion vary significantly even if they live at the same residential location due to their idiosyncratic activity-travel patterns. The results corroborate the claims in previous studies that using data aggregated over areas (e.g., census tracts) or focusing only on commuting trips (and thus ignoring individuals’ activity-travel patterns) may lead to erroneous assessments of individual exposures to traffic congestion or other environmental influences.
Air pollution is one of the greatest challenges cities are facing today and improving air quality is a pressing need to reduce negative health impacts. In order to efficiently evaluate which are the most appropriate policies to reduce the impact of urban pollution sources (such as road traffic), it is essential to conduct rigorous population exposure assessments. One of the main limitations associated with those studies is the lack of information about population distribution in the city along the day (population dynamics). The pervasive use of mobile devices in our daily lives opens new opportunities to gather large amounts of anonymized and passively collected geolocation data allowing the analysis of population activity and mobility patterns. This study presents a novel methodology to estimate population dynamics from mobile phone data based on a user-centric mobility model approach. The methodology was tested in the city of Madrid (Spain) to evaluate population exposure to NO2. A comparison with traditional census-based methods shows relevant discrepancies at disaggregated levels and highlights the need to incorporate mobility patterns into population exposure assessments.
In northern China, central heating, as an important source of urban particulate matter (UPM), causes more than half of the air pollution during the heating season and has significant spatial-temporal heterogeneity. Owing to the limitations of stationary air monitoring networks, few studies distinguish between heating/non-heating seasons and few have been conducted in urban areas. However, fixed monitoring cannot accurately capture the dynamic exposure of residents to UPM, and there is a lack of comprehensive evaluation of the factors affecting UPM. Therefore, this study used wearable Sniffer 4D equipment to monitor the concentrations of UPM (PM1, PM2.5, and PM10) in selected typical areas of Shenyang City from March 2019 to February 2020. A random forest model was combined with land use and point-of-interest data to analyze the contributions and marginal effects of multiple influences on UPM, in both heating and non-heating seasons. The results showed that in the eastern part of the study area, UPM showed completely opposite spatial distribution characteristics during the two seasons. The concentrations of UPM were higher during the heating season than during the non-heating season. The results indicated that temperature and humidity were important factors in diffusing UPM. The production and operation of boilers were important for the production of UPM. In two-dimensional landscape pattern indices, the percentage of forest and Shannon diversity index were the first and second most important factors, respectively. The three-dimensional pattern of buildings had important effects on the transport and diffusion of UPM (landscape height range >100, floor area ratio >1.3, and landscape volume density >5). Wearable devices could monitor the real situation of residents’ exposure to UPM and quantify the factors influencing the spatial-temporal distribution of UPM in an ecological sense. These results provide a scientific basis for urban planning and for health risk reduction for residents.
Urban air pollution is quite complex and exhibits significant spatial variability within communities. Traditional centralized monitoring captures temporal variability as well as the long-term trends of air pollution very well, but mapping spatial variability of air pollution in communities at high resolution would require large number of air quality monitors distributed across the community. Mobile monitoring complements stationary monitoring approaches by measuring pollution levels on accessible road networks in urban communities. This paper presents the application of an integrated mobile measurement and data analysis approach to study community-level air pollution patterns, separate the regional background and local contributions, and identify high pollution zones for black carbon (BC), ultrafine particles (UFP), and fine particulate matter (PM2.5). This study was conducted during different periods from 2017 to 2019 in three California cities (Richmond, Stockton, and Commerce) with diverse community and source characteristics.
The study found that traffic was the dominant primary source of air pollution in both urban and suburban settings. Urban areas adjacent to large area sources, such as ports and railyards had increased pollutant enhancements due to either direct or indirect emissions, while suburban areas containing unpaved roads were observed to have large PM2.5 enhancement due to the resuspended dust. The analysis disaggregated the contribution of regional and local sources to local air pollution and found that regional background sources contributed up to 75% of the PM2.5 concentrations, while local sources contributed more than half of BC and UFP. The study suggests that 15–30 repeated measurements may be sufficient to map the general air pollution patterns within the community, while some extreme high pollution zones can be identified with fewer repeats (5–10). These techniques can be used for initial screening of air pollution variability within the community, and help identify priority areas for conducting follow-up long-term air quality measurements. The techniques also informs the relative importance of regional and local actions to reduce community pollutant levels.
Accurately assessing individual exposures to traffic congestion and traffic-related emissions is important for evaluating the impact of traffic congestion on human health. Existing studies on individual exposures to traffic congestion focus mainly on commuting trips, while our understanding of individual exposures to traffic congestion during different types of trips is very limited. This study examines individual exposures to traffic congestion during various types of trips by using taxi GPS trajectory and POI datasets in Wuhan, China. We first infer individual trip purposes from the GPS trajectories based on the attractiveness of the POIs and Bayesian rules. Individual exposures to traffic congestion associated with different types of trips are then evaluated. We also estimate exposures to excessive traffic-related emissions due to traffic congestion associated with different types of trips. The results show that individual exposures to traffic congestion are more related to the space-time rhythm of traffic flows than the types of trips or activities conducted. The findings indicate that only considering commuting trips would underestimate individual exposures to traffic congestion and excessive traffic-related emissions. This study provides a more detailed picture of how people conducting different types of trips and activities are exposed to traffic congestion. It advances our understanding of individual exposures to activity-related traffic congestion in space and time.
Accurate individual exposure assessment to particulates in complex urban environments requires maps of PM2.5 concentration at high spatiotemporal resolution. Previous empirical researches of PM2.5 mapping usually have ignored the contextual influences of associated factors on pollution variation. This study presents a new thinking about spatial prediction of PM2.5 pollution based on the pollution scene assumption. Methodologically, pollution scenes are areas exert contextual influences on the spatiotemporal variety of air pollution and can be expressed by urban microenvironment dependence and temporal nonstationarity. Taking Changsha, China as a case, a two-stage modelling strategy of geographically weighted regression kriging (GWRK) was developed to validate the assumption based on a high-density sampling campaign and a fine-scale, manually interpreted urban microenvironment map. Our results confirm the potential existence of urban air pollution scene. PM2.5 concentration varies between urban microenvironments; pollution scene based GWRK is effective for high resolution mapping of PM2.5 concentration at the hourly scale and depicts more detailed spatial variations than traditional GWR in this study. This assumption and modelling strategy provide a promising way for mapping urban air pollution at high resolution which will further benefits works on exposure assessment and risk avoidance at fine scales.
With the near-ubiquitous presence of smartphones among urban dwellers in many parts of the world, we are living in an age where the public can act as continuous sensors of urban spaces. As such, data collected from GPS sensors in phones are particularly suited to support understanding human spatial behaviors in cities, and their potential for societal monitoring has been much anticipated. Yet, the field is still emerging and practical steps for utilizing smartphone-GPS in human behavior research remain unclear. Over a decade after the introduction of smartphones, we review the use of GPS data collected by these devices (smartphone-GPS data) as a tool for researching human behavior in cities. Using methods and findings from 96 papers that investigate human behaviors using smartphone-GPS data, we present seven application themes that describe domains where these data have been used thus far: sports and physical activity, environmental conditions, health and wellbeing, places and movement, neighborhoods and society, tourism, and single amenity use. We also describe the methodological factors, including parameters and variables, that have shaped how researchers have used smartphone-GPS data to understand relationships between pedestrian-scale human behaviors and urban environments. Based on these findings, we make recommendations for future researchers using smartphone-GPS data to understand relationships between humans and urban environments, at the pedestrian scale.
Introduction
Previous research suggested that an out-of-hospital cardiac arrest (OHCA) may be triggered by an exposure to ambient pollutants.
Objective
We investigated the link between OHCA and a short-term exposure to particulate matter (PM) and other pollutants, within extreme climate conditions in Israel and high PM.
Methods
In a case-crossover analysis, we analyzed all adult cases of OHCA in Israel during 2016–2017. The air-pollution and meteorology data were retrieved from the 132 monitoring stations. All associations at study were investigated using a lag-distributed regression and adjusted to temperature and humidity.
Results
There were 12401 OHCA cases. Patients experiencing OHCA were likely to be exposed to elevated levels of pollutants, specifically, nitrogen dioxide (NO2) and particulate matter of size ≤2.5 μm (PM2.5) several hours prior to an event, although both at borderline significance, i.e. odds ratio (OR) = 1.20 (95%CI 0.96; 1.51) and OR = 1.15 (95%CI 0.84; 1.60), respectively. An exposure to NO2 was independently associated with OHCA among males (OR = 1.39, 95%CI 0.96; 2.01) and if occurred during the midweek (OR = 1.43, 95%CI 1.03; 1.97). The adverse effect of PM10 was more evident during a weekend (OR = 2.36, 95%CI 0.88; 6.28), as opposed to working days (OR = 0.81, 95%CI 0.45; 1.44). Analysis stratified by regions suggested a spatial variability in pollution associated with OHCA.
Conclusions
Short-term exposure to high levels of pollution is adversely associated with OHCA independently of meteorological conditions. The magnitude of the effect is modified by patients' demography.
Main finding
Short-term exposure to high levels of pollution is adversely associated with OHCA. This effect is independent of temperature and humidity.
Atmospheric fine particulate matter (particulate matter with an aerodynamic diameter
$\le 2.5~\mu \text{m}$
in ambient air; PM
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) is a major pollutant causing regional air pollution and harm to human health. To monitor PM
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, Chinese industry authorities use data from a small number of fixed stations with uneven distribution. Pollution control is promoted through assessment and enforcement, which can include industry-wide shutdown measures harmful to local economic development. The objective of this study was to establish a fine particulate matter network (FPMN) of sensors based on the Internet of Things with low cost, high spatiotemporal resolution, flexible distribution points, large numbers, and high collection frequency. The FPMN-derived data, together with other multisource environment-related data, could be used to track and locate atmospheric pollutants and selectively identify source. This study adopted Chizhou, China as the research object. Specifically, the work included designing the FPMN, selecting locations for sensor placement on the basis of local weather, terrain, and land use, and using software/hardware collaborative calibration technology to ensure consistency between FPMN-derived data and National Control Station(NCS) data. The analysis revealed that FPMN data effectively reconstructed a reliable regional field of PM
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concentration with improved spatiotemporal accuracy. The research results will have great importance regarding the traceability of sources of PM
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pollution, analysis of pollution causes and transboundary pollution, optimization and adjustment of industrial layouts, and differentiation of the temporal and spatial control of pollution. Ultimately, the FPMN could directly support management and decision-making processes of local governments in relation to the environment and industry.
Comprehensive observations have been carried out in Beijing to investigate the impact of the Clean Air Action implemented in 2013 on changes in aerosol chemistry characteristics in heating seasons of 2016-2017 and 2017-2018. Results showed that PM2.5, SO2, NO2, NH3, O3 and CO concentrations decreased by 40.9%, 46.0%, 29.0%, 40.6%, 11.0% and 44.4%, respectively. Significant decreases were also observed for NO3⁻ (32.5%), SO4²⁻ (52.9%), NH4⁺ (56.0%), Cl⁻ (64.6%) and K⁺ (68.2%), on average. Enhanced PM2.5 pollution has changed from sulfate-driven to nitrate-driven. The decrease in SO2 was more significant than NO2 as a response to one reason of the larger decrease in SO4²⁻ concentration. The formation of sulfate was dominated by heterogeneous reactions in two heating seasons. Low pH could facilitate more efficient conversion of SO2 to sulfate. Ox played a much more important role in the formation of nitrate in the second heating season. Photochemical reactions contribute greatly to high nitrate concentrations in the daytime, especially in 2017-2018 heating season. The major source regions for sulfate and nitrate were identified by back trajectories and the potential source function (PSCF). More nitrate was brought into Beijing when air masses coming from polluted regions in the southwest prevailed in 2017-2018 heating season. Thus, regional joint prevention and control are of great importance in the achievement of an effective reduction in PM2.5 pollution in the future.
As one of the principal components of haze, fine particulate matter (PM2.5) 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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.
The rapid expansion of cities has caused enormous changes in the underlying surface, and coupled with the intensification of human activities, it has resulted in a series of ecological and environmental problems, such as urban heat island, air pollution, and so on. Through combining building height and air pollution source information, and using the computational fluid dynamics (CFD) model, this study simulated the distribution characteristics of wind speed, temperature and SO2 concentration under the condition of urban three-dimensional expansion, and then the analysis was performed on the influence of three-dimensional expansion on air environment diffusion during winter and spring in the Shenyang Tiexi District under the urban landscape pattern of 2005 and 2011. Monitoring data from 10 sampling sites were used to validate the model. The results showed that the R² values for wind speed, temperature and SO2 were 0.76, 0.79 and 0.64, respectively. The wind speed, temperature and SO2 concentration distribution pattern at the same height of the study area is obviously different because of the differences in urban building height, density and layout between 2005 and 2011. From 2005 to 2011, with the change in the urban architecture landscape pattern, the city corridor significantly changed.
Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.
The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants' clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants' respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants' concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NO x during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO 2 , which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O 3 and NO x) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while traffic emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects. ARTICLE HISTORY
Research on the relationship between built environment and PM2.5 has attracted notable attention during the past decades. However, previous studies were less to test the spatial-temporal heterogeneity of on-road PM2.5 and its related factors at micro scale. To this end, collecting high-resolution PM2.5 data by mobile monitoring along different roads in Guangzhou, China, this paper explored the spatial-temporal heterogeneity of the relationship between built environment and on-road PM2.5 during the morning (7–9 am) and evening (7–9 pm) rush hours. Semi-variogram method and geographically weighted regression (GWR) model were utilized to reveal the non-stationarity associations among the large spatial dataset. In terms of temporal heterogeneity, the results showed that the spatial independent radii of on-road PM2.5 were 17 m and 21 m for morning and evening rush hours respectively. The aggregated median value of PM2.5 in the morning rush hours was 34.95 μg/m³, while the evening was up to 55.49 μg/m³. There were more significant factors of street conditions impact on on-road PM2.5 in the morning while more significant factors of land use and centrality that reflecting the cumulative effect of daily human activities with smaller buffer thresholds in the evening. In terms of spatial heterogeneity, GWR models achieved much better performance than the global ones of multivariate regression models with lower AICc, RMSE and higher adjusted R², explaining 10–69% of variance across different roads and rush hours. There was a high degree of spatial heterogeneity that the leading factors were different along various roads on rush hours. The results indicated that the policies and interventions should be more targeted to improve the on-road air environment and reduce personal exposure according to the spatial-temporal geographical context. It can be adopted to provide more realistic and practical guides for urban planning and environmental pollution control.
Particulate matter that <2.5 µm in aerodynamic diameter (PM2.5) has been recognized as one of the principal pollutants that degrades air quality and increases health burdens. In this study, we employ the MLR and GWR modelling method to obtain estimation models for PM2.5 with a set of land use/landscape metrics as predictor variables. The study focused on investigating the influence of urban land use and landscape pattern on PM2.5 spatial variation, specifically, on identification of influential landscape classes/types that regulate PM2.5 concentration levels. The spatial PM2.5 concentration in the compact urban scenario of Hong Kong was sampled by conducting a series of mobile monitoring campaigns. The Local Climate Zone (LCZ) Scheme and World Urban Database and Portal Tools (WUDAPT) level 0 database were adopted as the basis of the calculation of land use/landscape metrics. These metrics were then adopted as the predictors to explain the spatial variations in PM2.5. 62% of the variance in PM2.5 can be explained by the resultant GWR model using only five land use/landscape classes, and without using any traffic-related variables or data from emission inventory. The findings can inform the urban planning strategies for mitigating air pollution and also indicate the usefulness of LCZ and WUDAPT in estimating the spatial variation of urban air quality.
An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) is crucial to hazard assessment and epidemiology. This study integrated annual data from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM2.5 concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM2.5 exposure. Using Shanghai as a case study, the annual average indoor PM2.5 concentration was estimated to be 29.3 ± 27.1 μg/m³ (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM2.5 exposure was estimated to be 32.1 ± 13.9 μg/m³ (n = 365), with indoor PM2.5 contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM2.5 makes a significant contribution to indoor PM2.5, outdoor PM2.5 was responsible for most of the exposure in Shanghai. A heatmap of PM2.5 exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation.
People pay greater attention to air quality which is closely related to their health, especially in developing countries. Air quality is part of Chinese weather forecast, and the government has developed air quality monitoring systems, built high quality monitoring stations (HQMS). With the data from HQMS, many companies and research institutes demonstrate accurate air pollution map on the Internet, which is valuable for many issues related to air quality including exposure modeling and urban planning. Due to the high equipment and operating costs, the distribution of HQMS is too uneven and sparse to achieve high resolution air pollution map. Thus, people try to deploy a large number of high precision sensors in and around the city for detecting air pollution. However, these sensors require frequent calibrations with the HQMS to maintain data accuracy. On the other side, to reduce the cost of sensor deployment, people begin to use mobile sensors instead of fixed sensors, which make sensor route planning a very important issue. Nevertheless, existing works on route planning of mobile sensors largely focus on data reconstruction, which either ignores calibration or views it as an independent problem. In this paper, we propose a novel scheme to improve the accuracy of data reconstruction, which jointly considers sensor calibration and data reconstruction in route planning for mobile sensors. We formulate a novel sensor route planning problem (SRPP) which aims to maximize the mutual information and guarantee the accuracy of measurements through sensor calibration. We also propose a heuristic algorithm to solve the SRPP, which supports calibration between mobile sensors and HQMS in route planning. Extensive simulation results well justify the effectiveness of our approach that can reduce 83% root mean square error (RMSE) on average compared with the traditional approach.
Epidemiological studies often assign outdoor air pollution concentrations to residential locations without accounting for mobility patterns. In this study, we examined how neighborhood characteristics may influence differences in exposure assessments between outdoor residential concentrations and mobility-based exposures. To do this, we linked residential location and mobility data to exposure surfaces for NO2, PM2.5, and ultrafine particles in Montreal, Canada for 5452 people in 2016. Mobility data were collected using the MTL Trajet smartphone application (mean: 16 days/subject). Generalized additive models were used to identify important neighborhood predictors of differences between residential and mobility-based exposures and included residential distances to highways, traffic counts within 500 meters of the residence, neighborhood walkability, median income, and unemployment rate. Final models including these parameters provided unbiased estimates of differences between residential and mobility-based exposures with small root mean square error values in 10-fold cross validation samples. In general, our findings suggest that differences between residential and mobility-based exposures are not evenly distributed across cities and are greater for pollutants with higher spatial variability like NO2. It may be possible to use neighborhood characteristics to predict the magnitude and direction of this error to better understand its likely impact on risk estimates in epidemiological analyses.