Mapping urban form into local climate zones for the continental US from 1986-2020.

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people's activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling and cloud-enabled modeling - an approach that could be generalized to other countries and continents. Our model achieved good performance: 0.76 overall accuracy (0.55-0.96 class-wise F1 scores). To our knowledge, this is the first high-resolution, longitudinal LCZ map for the continental US. Our work may be useful for a variety of fields including earth system science, urban planning, and public health.

Leveraging Citizen Science and Low-Cost Sensors to Characterize Air Pollution Exposure of Disadvantaged Communities in Southern California.

Assessing exposure to fine particulate matter (PM2.5) across disadvantaged communities is understudied, and the air monitoring network is inadequate. We leveraged emerging low-cost sensors (PurpleAir) and engaged community residents to develop a community-based monitoring program across disadvantaged communities (high proportions of low-income and minority populations) in Southern California. We recruited 22 households from 8 communities to measure residential outdoor PM2.5 concentrations from June 2021 to December 2021. We identified the spatial and temporal patterns of PM2.5 measurements as well as the relationship between the total PM2.5 measurements and diesel PM emissions. We found that communities with a higher percentage of Hispanic and African American population and higher rates of unemployment, poverty, and housing burden were exposed to higher PM2.5 concentrations. The average PM2.5 concentrations in winter (25.8 µg/m3) were much higher compared with the summer concentrations (12.4 µg/m3). We also identified valuable hour-of-day and day-of-week patterns among disadvantaged communities. Our results suggest that the built environment can be targeted to reduce the exposure disparity. Integrating low-cost sensors into a citizen-science-based air monitoring program has promising applications to resolve monitoring disparity and capture "hotspots" to inform emission control and urban planning policies, thus improving exposure assessment and promoting environmental justice.

Short-term exposure to fine particulate matter and its constituents may affect renal function via oxidative stress: A longitudinal panel study.

Exposure to fine particulate matter (PM2.5) has been reported to increase the risks of chronic kidney disease. However, limited research has assessed the effect of PM2.5 and its constituents on renal function, and the underlying mechanism has not been well characterized. We aimed to evaluate the association of PM2.5 and its constituents with kidney indicators and to explore the roles of systematic oxidative stress and inflammation in the association. We conducted a longitudinal panel study among 35 healthy adults before-, intra- and after-the 2019 Wuhan Military World Games. We repeatedly measured 6 renal function parameters and 5 circulating biomarkers of oxidative stress and inflammation at 6 rounds of follow-ups. We monitored hourly personal PM2.5 concentrations with 3 consecutive days and measured 10 metals (metalloids) and 16 polycyclic aromatic hydrocarbons (PAHs) components. The linear mixed-effect models were applied to examine the association between PM2.5 and renal function parameters, and the mediation analysis was performed to explore potential bio-pathways. PM2.5 concentrations across Wuhan showed a slight decrease during the Military Games. We observed significant associations between elevated blood urea nitrogen (BUN) levels and PM2.5 and its several metals and PAHs components. For an interquartile range (IQR) increase of PM2.5, BUN increased 0.42 mmol/L (95% CI: 0.14 to 0.69). On average, an IQR higher of lead (Pb), cadmium (Cd), arsenic (As), selenium (Se), thallium (Tl) and Indeno (1,2,3-cd) pyrene (IPY) were associated with 0.90, 0.65, 0.29, 0.27, 0.26 and 0.90 mmol/L increment of BUN, respectively. Moreover, superoxide dismutase was positively associated with PM2.5 and mediated 18.24% association. Our research indicated that exposure to PM2.5 might affect renal function by activating oxidative stress pathways, in which the constituents of Pb, Cd, As, Se, Tl and IPY might contribute to the associations.

Association of depressive symptoms with ambient PM2.5 in middle-aged and elderly Chinese adults: A cross-sectional study from the China health and Retirement Longitudinal Study wave 4.

Depressive symptoms have become a serious public health issue worldwide. Several studies showed that air pollution, especially fine particulate matter (PM2.5), may be a risk factor of mental disorders. However, existing studies reported inconsistent results and little evidence is available in developing countries, like China. To fill the gap, in this study, we explored the relationship between ambient PM2.5 exposure and depressive symptoms among middle-aged and elderly Chinese adults in the Chinese Health and Retirement Longitudinal Study (CHARLS). The social and demographic variables and depressive symptoms were obtained from the Wave4 of CHARLS in 2018. PM2.5 concentrations were obtained from the national urban air quality real-time release platform of China Environmental Monitoring Station. We applied generalized linear mixed models to determine the association between PM2.5 exposure and depressive symptoms. A total of 15,105 middle-aged and elderly adults from CHARLS Wave4 were included in the analyses. We found positive impact of ambient PM2.5 on depressive symptoms for the exposure windows of 30-day, 60-day, 120-day, 180-day, 1-year and 2-year. The most significant increase was observed for 180-day moving average. For every 10 µg/m3 increment in PM2.5 exposure, the incidence of depressive symptoms increased by 9% (OR = 1.09; 95%CI: 1.05, 1.14) after adjusting for age, sex and residence. In interaction analyses, we found PM2.5 had weaker effect on depressive symptoms among people who used to drink alcohol (OR = 1.05; 95%CI: 1.00, 1.10) and exercise (OR = 1.10; 95%CI: 1.02, 1.18). People living in western China (OR = 1.09; 95%CI: 1.03, 1.16) were more vulnerable than those living in eastern China (OR = 0.99; 95%CI: 0.94, 1.05). In conclusion, exposure to PM2.5 was significantly associated with depressive symptoms in middle-aged and elder Chinese adults, particularly for people who never drink, with lower physical activity levels, or lived in western China.

Acute effect of fine particulate matter on blood pressure, heart rate and related inflammation biomarkers: A panel study in healthy adults.

Epidemiological evidence of short-term fine particulate matter (PM2.5) exposure on blood pressure (BP), heart rate (HR) and related inflammation biomarkers has been inconsistent. We aimed to explore the acute effect of PM2.5 on BP, HR and the mediation effect of related inflammation biomarkers. A total of 32 healthy college students were recruited to perform 4 h of exposure at two sites with different PM2.5 concentrations in Wuhan between May 2019 and June 2019. The individual levels of PM2.5 concentration, BP and HR were measured hourly for each participant. Blood was drawn from each participant after each visit and we measured the levels of inflammation markers, including serum high-sensitivity C-reactive protein and plasma fibrinogen. Linear mixed-effect models were to explore the acute effect of PM2.5 exposure on BP, HR, and related inflammation biomarkers. In addition, we evaluated related inflammation biomarkers as the mediator in the association of PM2.5 and cardiovascular health indicators. The results showed that a 10 µg/m3 increment in PM2.5 concentration was associated with an increase of 0.84 (95% CI: 0.54, 1.15) beats/min (bpm) in HR and a 3.52% (95% CI: 1.60%, 5.48%) increase in fibrinogen. The lag effect model showed that the strongest effect on HR was observed at lag 3 h of PM2.5 exposure [1.96 bpm (95% CI: 1.19, 2.75)], but for fibrinogen, delayed exposure attenuated the association. Increased fibrinogen levels may account for 39.07% (P = 0.44) of the elevated HR by PM2.5. Null association was observed when it comes to short-term PM2.5 exposure and BP. Short-term exposure to PM2.5 was associated with elevated HR and increased fibrinogen levels. But our finding was not enough to suggest that exposure to PM2.5 might induce adverse cardiovascular effects by the pathway of inflammation.

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment.

National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO2, PM2.5, O3, CO, PM10, SO2) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R2 increased 0.02-0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R2 = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.

Combining expert and crowd-sourced training data to map urban form and functions for the continental US.

Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.

Land Use Regression models for 60 volatile organic compounds: Comparing Google Point of Interest (POI) and city permit data.

Land Use Regression (LUR) models of Volatile Organic Compounds (VOC) normally focus on land use (e.g., industrial area) or transportation facilities (e.g., roadway); here, we incorporate area sources (e.g., gas stations) from city permitting data and Google Point of Interest (POI) data to compare model performance. We used measurements from 50 community-based sampling locations (2013-2015) in Minneapolis, MN, USA to develop LUR models for 60 VOCs. We used three sets of independent variables: (1) base-case models with land use and transportation variables, (2) models that add area source variables from local business permit data, and (3) models that use Google POI data for area sources. The models with Google POI data performed best; for example, the total VOC (TVOC) model has better goodness-of-fit (adj-R2: 0.56; Root Mean Square Error [RMSE]: 0.32 µg/m3) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). Area source variables were selected in over two thirds of models among the 60 VOCs at small-scale buffer sizes (e.g., 25 m-500 m). Our work suggests that VOC LUR models can be developed using community-based sampling and that models improve by including area sources as measured by business permit and Google POI data.