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Sprague NL, Uong SP, Kelsall NC, Jacobowitz AL, Quinn JW, Keyes KM, Rundle AG. Using geographic effect measure modification to examine socioeconomic-related surface temperature disparities in New York City. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024:10.1038/s41370-024-00714-6. [PMID: 39179752 DOI: 10.1038/s41370-024-00714-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 08/13/2024] [Accepted: 08/14/2024] [Indexed: 08/26/2024]
Abstract
BACKGROUND Lower socioeconomic (SES) communities are more likely to be situated in urban heat islands and have higher heat exposures than their higher SES counterparts, and this inequality is expected to intensify due to climate change. OBJECTIVES To examine the relationship between surface temperatures and SES in New York City (NYC) by employing a novel analytical approach. Through incorporating modifiable features, this study aims to identify potential locations where mitigation interventions can be implemented to reduce heat disparities associated with SES. METHODS Using the 2013-2017 American Community Survey, U.S Landsat-8 Analysis Ready Data surface temperatures (measured on 8/12/2016), and the NYC Land Cover Dataset at the census tract level (2098 tracts), this study examines the association between two components of tract-level SES (percentage of individuals living below the poverty line and the percentage of individuals without a high school degree) and summer day surface temperature in NYC. First, we examine this association with an unrestricted NYC linear regression, examining the city-wide association between the two SES facets and summer surface temperature, with additional models adjusting for altitude, shoreline, and nature-cover. Then, we assess geographic effect measure modification by employing the same models to three supplemental regression model strategies (borough-restricted and community district-restricted linear regressions, and geographically weighted regression (GWR)) that examined associations within smaller intra-city areas. RESULTS All regression strategies identified areas where lower neighborhood SES composition is associated with higher summer day surface temperatures. The unrestricted NYC regressions revealed widespread disparities, while the borough-restricted and community district-restricted regressions identified specific political boundaries within which these disparities existed. The GWR, addressing spatial autocorrelation, identified significant socioeconomic heat disparities in locations such as northwest Bronx, central Brooklyn, and uptown Manhattan. These findings underscore the need for targeted policies and community interventions, including equitable urban planning and cooling strategies, to mitigate heat exposure in vulnerable neighborhoods. IMPACT STATEMENT This study redefines previous research on urban socioeconomic disparities in heat exposure by investigating both modifiable (nature cover) and non-modifiable (altitude and shoreline) built environment factors affecting local temperatures at the census tract level in New York City. Through a novel analytical approach, the research aims to highlight intervention opportunities to mitigate heat disparities related to socioeconomic status. By examining the association between surface temperatures and socioeconomic status, as well as investigating different geographic and governmental scales, this study offers actionable insights for policymakers and community members to address heat exposure inequalities effectively across different administrative boundaries. The objective is to pinpoint potential sites for reducing socioeconomic heat exposure disparities at various geographic and political levels.
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Affiliation(s)
- Nadav L Sprague
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA.
| | - Stephen P Uong
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - Nora C Kelsall
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - Ahuva L Jacobowitz
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - James W Quinn
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - Katherine M Keyes
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - Andrew G Rundle
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
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Bussalleu A, Hoek G, Kloog I, Probst-Hensch N, Röösli M, de Hoogh K. Modelling Europe-wide fine resolution daily ambient temperature for 2003-2020 using machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 928:172454. [PMID: 38636867 DOI: 10.1016/j.scitotenv.2024.172454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003-2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.
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Affiliation(s)
- Alonso Bussalleu
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland.
| | - Gerard Hoek
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Nicole Probst-Hensch
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Martin Röösli
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Kees de Hoogh
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
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Sprague NL, Uong SP, Jacobowitz AL, Packard SE, Quinn JW, Keyes KM, Rundle AG. Examining racial and ethnic heat exposure disparities in New York City (NYC) across different spatial and political scales through geographic effect measure modification. ENVIRONMENTAL RESEARCH 2024; 250:118521. [PMID: 38382663 PMCID: PMC11102848 DOI: 10.1016/j.envres.2024.118521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 02/02/2024] [Accepted: 02/17/2024] [Indexed: 02/23/2024]
Abstract
Structural racism in the United States has resulted in neighborhoods with higher proportions of non-Hispanic Black (Black) or Hispanic/Latine residents having more features that intensify, and less that cool, the local-heat environment. This study identifies areas of New York City (NYC) where racial/ethnic heat exposure disparities are concentrated. We analyzed data from the 2013-2017 American Community Survey, U.S Landsat-8 Analysis Ready Data on summer surface temperatures, and NYC Land Cover Dataset at the census tract-level (n = 2098). Four cross-sectional regression modeling strategies were used to estimate the overall City-wide association, and associations across smaller intra-city areas, between tract-level percent of Black and percent Hispanic/Latine residents and summer day surface temperature, adjusting for altitude, shoreline, and nature-cover: overall NYC linear, borough-specific linear, Community District-specific linear, and geographically weighted regression models. All three linear regressions identified associations between neighborhood racial and ethnic composition and summer day surface temperatures. The geographically weighted regression models, which address the issue of spatial autocorrelation, identified specific locations (such as northwest Bronx, central Brooklyn, and uptown Manhattan) within which racial and ethnic disparities for heat exposures are concentrated. Through examining the overall effects and geographic effect measure modification across spatial scales, the results of this study identify specific geographic areas for intervention to mitigate heat exposure disparities experienced by Black and Hispanic/Latine NYC residents.
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Affiliation(s)
- Nadav L Sprague
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA.
| | - Stephen P Uong
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - Ahuva L Jacobowitz
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - Samuel E Packard
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - James W Quinn
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - Katherine M Keyes
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
| | - Andrew G Rundle
- Department of Epidemiology, Columbia Mailman School of Public Health, 722 W 168th St., New York, NY, 10032, USA
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Kloog I, Zhang X. Methods to Advance Climate Science in Respiratory Health: Satellite-Based Environmental Modeling for Temperature Exposure Assessment in Epidemiological Studies. Immunol Allergy Clin North Am 2024; 44:97-107. [PMID: 37973263 DOI: 10.1016/j.iac.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Climate change is a major concern with significant impacts on human health including respiratory outcomes, particularly through changes in air temperature. The rise in global temperature has led to an increase in heat waves and extreme weather events, which pose serious risks to respiratory health. Accurately assessing the effects of air temperature on respiratory health requires a comprehensive approach that incorporates fine-scale exposure assessment to characterize the geospatial environment impacting population health. Recent advances in open-source earth observation data have allowed for improved exposure assessment through temperature modeling.
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Affiliation(s)
- Itai Kloog
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Geography and Environmental Development, Ben-Gurion University, Beer Sheva, Israel; Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xueying Zhang
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Pediatrics, The Kravis Children's Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Chawala P, Priyan R S, Sm SN. Climatology and landscape determinants of AOD, SO 2 and NO 2 over Indo-Gangetic Plain. ENVIRONMENTAL RESEARCH 2023; 220:115125. [PMID: 36592806 DOI: 10.1016/j.envres.2022.115125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 12/12/2022] [Accepted: 12/18/2022] [Indexed: 06/17/2023]
Abstract
Indo-Gangetic Plains (IGP) experiences high loading of particulate and gaseous pollutants all year around and is considered to be the most polluted regions of India. Understanding the effect of landscape determinants on air pollution in IGP regions is crucial to make its environment sustainable. We examined satellite retrievals of OMI NO2 and SO2, and MODIS AOD to analyse the long-term trend, spatio-seasonal pattern and dynamics of aerosols, NO2 and SO2 over three IGP regions, namely Upper Indo-Gangetic plain (UIGP), Middle Indo-Gangetic plain (MIGP) and Lower Indo-Gangetic plain (LIGP) over the period 2005-2019. IGP experienced an overall increment in AOD (R2 = 0.63) and SO2 (R2 = 0.67) values, with LIGP (AOD, R2 = 0.8 & SO2, R2 = 0.8) experiencing the largest rate of enhancement. The levels of NO2 (R2 = 0.2) experienced a decrement after 2012 (owing to implementation of vehicle emission policy) except in MIGP, with UIGP (R2 = 0.23) exhibiting the largest rate of decrement. Seasonal heterogeneity in the nature of sources was observed over IGP regions. AOD (0.61 ± 0.1) and NO2 value (3.82 ± 0.98 × 1015 molecules/cm2) were found highest during post-monsoon in UIGP owing to crop residue burning activity. The value of NO2 (3.8 ± 1.4 × 1015 molecules/cm2) in MIGP was found highest during pre-monsoon due to high consumption of coal in power plants for summer cooling demand. The highest SO2 level (0.09 ± 0.06 DU) was observed during post-monsoon in UIGP, as a large number of brick kilns are fired during this period. Correlations among landscape determinants and pollutants revealed that topography is the dominant variable that affect the spatial pattern of AOD compared to vegetation and land use. Lower elevation tends to have high AOD values compared to higher elevation. Vegetation-AOD relationship showed an inverse association in IGP regions and is influenced by factors such as seasonal meteorology and size of the airborne particles. Vegetation possesses positive relationship with SO2 and NO2, implying no pollution abatement effect on SO2 and NO2 pollutants. Built-up change has deteriorating effect as well as quenching effect on pollutants. Increase in built terrain have deteriorated the air quality in UIGP whereas it favored in suppressing the aerosol level in LIGP.
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Affiliation(s)
- Pratika Chawala
- Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, 600 036, India.
| | - Shanmuga Priyan R
- Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, 600 036, India.
| | - Shiva Nagendra Sm
- Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, 600 036, India
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6
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Nikolaou N, Dallavalle M, Stafoggia M, Bouwer LM, Peters A, Chen K, Wolf K, Schneider A. High-resolution spatiotemporal modeling of daily near-surface air temperature in Germany over the period 2000-2020. ENVIRONMENTAL RESEARCH 2023; 219:115062. [PMID: 36535393 DOI: 10.1016/j.envres.2022.115062] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The commonly used weather stations cannot fully capture the spatiotemporal variability of near-surface air temperature (Tair), leading to exposure misclassification and biased health effect estimates. We aimed to improve the spatiotemporal coverage of Tair data in Germany by using multi-stage modeling to estimate daily 1 × 1 km minimum (Tmin), mean (Tmean), maximum (Tmax) Tair and diurnal Tair range during 2000-2020. We used weather station Tair observations, satellite-based land surface temperature (LST), elevation, vegetation and various land use predictors. In the first stage, we built a linear mixed model with daily random intercepts and slopes for LST adjusted for several spatial predictors to estimate Tair from cells with both Tair and LST available. In the second stage, we used this model to predict Tair for cells with only LST available. In the third stage, we regressed the second stage predictions against interpolated Tair values to obtain Tair countrywide. All models achieved high accuracy (0.91 ≤ R2 ≤ 0.98) and low errors (1.03 °C ≤ Root Mean Square Error (RMSE) ≤ 2.02 °C). Validation with external data confirmed the good performance, locally, i.e., in Augsburg for all models (0.74 ≤ R2 ≤ 0.99, 0.87 °C ≤ RMSE ≤ 2.05 °C) and countrywide, for the Tmean model (0.71 ≤ R2 ≤ 0.99, 0.79 °C ≤ RMSE ≤ 1.19 °C). Annual Tmean averages ranged from 8.56 °C to 10.42 °C with the years beyond 2016 being constantly hotter than the 21-year average. The spatial variability within Germany exceeded 15 °C annually on average following patterns including mountains, rivers and urbanization. Using a case study, we showed that modeling leads to broader Tair variability representation for exposure assessment of participants in health cohorts. Our results indicate the proposed models as suitable for estimating nationwide Tair at high resolution. Our product is critical for temperature-based epidemiological studies and is also available for other research purposes.
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Affiliation(s)
- Nikolaos Nikolaou
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, LMU Munich, Munich, Germany.
| | - Marco Dallavalle
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | - Laurens M Bouwer
- Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
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Lei Y, Wang Z, Xu H, Feng R, Zhang N, Zhang Y, Du W, Zhang Q, Wang Q, Li L, Qu L, Hang Ho SS, Shen Z, Cao J. Characteristics and health risks of parent, alkylated, and oxygenated PAHs and their contributions to reactive oxygen species from PM 2.5 vehicular emissions in the longest tunnel in downtown Xi'an, China. ENVIRONMENTAL RESEARCH 2022; 212:113357. [PMID: 35580669 DOI: 10.1016/j.envres.2022.113357] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/30/2022] [Accepted: 04/20/2022] [Indexed: 06/15/2023]
Abstract
A vehicular emission study was conducted in the longest inner-city tunnel in Xi'an, northwestern China in four time periods (I: 07:30-10:30, II: 11:00-14:00, III: 16:30-19:30, and IV: 20:00-23:00 LST). A sum of 40 PAHs, including parent (p-PAHs), alkylated (a-PAHs), and oxygenated (o-PAHs) in fine particulate matter (PM2.5) were quantified. The relationships between the PAHs and the formation of reactive oxygen species (ROS) were also studied. The average total quantified PAHs concentration was 236.3 ± 48.3 ng m-3. The p-PAHs were found to be the most dominated group, accounting for an average of 88.1% of the total quantified PAHs, followed by a-PAHs (6.1%) and o-PAHs (5.8%). On the base of the number of aromatic rings, the groups of ≤5 rings (92.5 ± 1.2%) had higher fractions than the high ones (≥6 rings, 7.5 ± 1.2%) for pPAHs. Diurnal variations of PAHs subgroups exhibited the highest levels in Period III, consistent with the largest traffic counts in evening rush hours. However, less reduction of few PAHs in the night period demonstrates that the emissions of compressed natural gas (CNG) and methanol-fueled vehicles cannot be ignored while their contribution increased. High ROS activity levels were observed in the traffic-dominated samples, implying the potential oxidative damages to humans. Additionally, diurnal variation of the ROS activity was consistent with the total quantified PAHs and toxic equivalency of benzo[a]pyrene. Good correlations (R > 0.6, p < 0.05) were seen between individual groups of PAHs (especially for 3-5 rings p-PAHs, 4 rings a-PAHs, and 2-3 rings o-PAHs) and ROS activity, supporting that the vehicular emitted PAHs possibly initiate oxidative stress. The multiple linear regression analysis further illustrated that chrysene contributed the highest (25.0%) to ROS activity. In addition to highlighting the potential hazards to the PAHs from the vehicular emission, their roles to mitigate the health effects by formations of ROS were firstly reported in northwestern China.
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Affiliation(s)
- Yali Lei
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; Key Lab of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, 200241, China
| | - Zexuan Wang
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Hongmei Xu
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710049, China.
| | - Rong Feng
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ningning Zhang
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710049, China
| | - Yue Zhang
- Henan Research Academy of Ecological and Environmental Sciences, Zhengzhou, 450003, China
| | - Wei Du
- Key Lab of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, 200241, China
| | - Qian Zhang
- Key Laboratory of Northwest Resource, Environment and Ecology, MOE, Xi'an University of Architecture and Technology, Xi'an, 710055, China
| | - Qiyuan Wang
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710049, China
| | - Lijuan Li
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710049, China
| | - Linli Qu
- Hong Kong Premium Services and Research Laboratory, Kowloon, Hong Kong SAR, China
| | - Steven Sai Hang Ho
- Division of Atmospheric Sciences, Desert Research Institute, Reno, NV, 89512, United States
| | - Zhenxing Shen
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Junji Cao
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710049, China
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Estimation of Daily and Instantaneous Near-Surface Air Temperature from MODIS Data Using Machine Learning Methods in the Jingjinji Area of China. REMOTE SENSING 2022. [DOI: 10.3390/rs14081916] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Meteorologically observed air temperature (Ta) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface Ta on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous Ta when satellites overpass. This study aims to produce both daily and instantaneous Ta datasets at 1 km resolution for the Jingjinji area, China during 2018–2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of Ta estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 °C, mean absolute error (MAE) of 0.94 °C, daytime instantaneous RMSE of 1.88 °C, MAE of 1.35 °C, nighttime instantaneous RMSE of 2.47 °C, and MAE of 1.83 °C. The corresponding R2 was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating Ta. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous Ta at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration.
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High-Resolution Estimation of Monthly Air Temperature from Joint Modeling of In Situ Measurements and Gridded Temperature Data. CLIMATE 2022. [DOI: 10.3390/cli10030047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Surface air temperature is an important variable in quantifying extreme heat, but high-resolution temporal and spatial measurement is limited by sparse climate-data stations. As a result, hyperlocal models of extreme heat involve intensive physical data collection efforts or analyze satellite-derived land-surface temperature instead. We developed a geostatistical model that integrates in situ climate-quality temperature records, gridded temperature data, land-surface temperature estimates, and spatially consistent covariates to predict monthly averaged daily maximum surface-air temperatures at spatial resolutions up to 30 m. We trained and validated the model using data from North Carolina. The fitted model showed strong predictive performance with a mean absolute error of 1.61 ∘F across all summer months and a correlation coefficient of 0.75 against an independent hyperlocal temperature model for the city of Durham. We show that the proposed model framework is highly scalable and capable of producing realistic temperature fields across a variety of physiographic settings, even in areas where no climate-quality data stations are available.
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Jin Z, Ma Y, Chu L, Liu Y, Dubrow R, Chen K. Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model. ENVIRONMENTAL RESEARCH 2022; 204:111960. [PMID: 34464620 DOI: 10.1016/j.envres.2021.111960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1 × 1 km2. The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. Various spatial predictors were included as adjustment variables in all the base models, including land cover type, normalized difference vegetation index (NDVI), and elevation. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Although each base model performed well, the two machine learning models (RF [R2 = 0.97], XGBoost [R2 = 0.98]) had better performance than the two regression models (GAM [R2 = 0.95], GAMM [R2 = 0.96]). In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, NDVI, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment in future epidemiological studies.
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Affiliation(s)
- Zhihao Jin
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Yiqun Ma
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Lingzhi Chu
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Robert Dubrow
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA.
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11
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Gutiérrez‐Avila I, Arfer KB, Wong S, Rush J, Kloog I, Just AC. A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003 to 2019. INTERNATIONAL JOURNAL OF CLIMATOLOGY : A JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2021; 41:4095-4111. [PMID: 34248276 PMCID: PMC8251982 DOI: 10.1002/joc.7060] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/31/2021] [Accepted: 02/13/2021] [Indexed: 05/05/2023]
Abstract
While weather stations generally capture near-surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta-related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite-based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003-2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite-hybrid mixed-effects model for each year, regressing Ta measurements against land use terms, day-specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10-fold cross-validation at withheld stations. Across all years, the root-mean-square error ranged from 0.92 to 1.92 K and the R 2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high-quality Ta estimates for epidemiology studies in the MCM region.
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Affiliation(s)
- Iván Gutiérrez‐Avila
- Department of Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Kodi B. Arfer
- Department of Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Sandy Wong
- Department of GeographyFlorida State University (FSU)TallahasseeFloridaUSA
| | - Johnathan Rush
- Department of Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Itai Kloog
- Department of Geography and Environmental DevelopmentBen‐Gurion University of the NegevBeershebaIsrael
| | - Allan C. Just
- Department of Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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12
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Zhang S, Breitner S, Cascio WE, Devlin RB, Neas LM, Ward-Caviness C, Diaz-Sanchez D, Kraus WE, Hauser ER, Schwartz J, Peters A, Schneider A. Association between short-term exposure to ambient fine particulate matter and myocardial injury in the CATHGEN cohort. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 275:116663. [PMID: 33581627 DOI: 10.1016/j.envpol.2021.116663] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/24/2020] [Accepted: 02/02/2021] [Indexed: 06/12/2023]
Abstract
Exposure to fine particulate matter (PM2.5) has been associated with a higher risk for coronary events. Elevated circulating cardiac troponins (cTn) are suggestive of myocardial injury in both ischemic and non-ischemic conditions. However, little is known about the association between PM2.5 and cTn. In this study, we investigated short-term PM2.5 effects on cardiac troponin T (cTnT), as well as N-terminal-pro brain natriuretic peptide (NT-pro BNP) and inflammatory biomarkers among cardiac catheterized participants. We analyzed 7444 plasma cTnT measurements in 2732 participants who presented to Duke University Hospital with myocardial infarction symptoms between 2001 and 2012, partly along with measurements of NT-pro BNP and inflammatory biomarkers. Daily PM2.5 concentrations were predicted by a neural network-based hybrid model and were assigned to participants' residential addresses. We applied generalized estimating equations to assess associations of PM2.5 with biomarker levels and the risk of a positive cTnT test (cTnT > 0.1 ng/mL). The median plasma cTnT concentration at presentation was 0.05 ng/mL and the prevalence of a positive cTnT test was 35.4%. For an interquartile range (7.6 μg/m3) increase in PM2.5 on the previous day, cTnT concentrations increased by 7.7% (95% CI: 3.4-12.3) and the odds ratio of a positive cTnT test was 1.08 (1.01-1.16). Participants under 60 years (effect estimate: 15.2%; 95% CI: 7.4-23.5) or living in rural areas (12.3%; 95% CI: 4.8-20.3) were more susceptible. There was evidence for increases in fibrinogen and NT-pro BNP associated with elevated PM2.5 on the concurrent and previous two days. Our study suggests that acute PM2.5 exposure may elevate indicators of myocardial tissue damage. This finding substantiates the association of air pollution exposure with adverse cardiovascular events.
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Affiliation(s)
- Siqi Zhang
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.
| | - Susanne Breitner
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Wayne E Cascio
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Robert B Devlin
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Lucas M Neas
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Cavin Ward-Caviness
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - David Diaz-Sanchez
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - William E Kraus
- Duke Molecular Physiology Institute, School of Medicine, Duke University, Durham, NC, USA
| | - Elizabeth R Hauser
- Duke Molecular Physiology Institute, School of Medicine, Duke University, Durham, NC, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
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Correlation Analysis between Air Temperature and MODIS Land Surface Temperature and Prediction of Air Temperature Using TensorFlow Long Short-Term Memory for the Period of Occurrence of Cold and Heat Waves. REMOTE SENSING 2020. [DOI: 10.3390/rs12193231] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The purpose of this study is to analyze the correlation between surface air temperature (SAT) and land surface temperature (LST) based on land use when heat and cold waves occur and to predict the distribution of SAT using the long short-term memory (LSTM) of TensorFlow. For the correlation analysis, the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime LST and maximum, minimum, and mean SAT were measured at 79 weather stations of the Korea Meteorological Administration (KMA) from 2008 to 2018. As a result of the correlation analysis between SAT and LST, the maximum SAT (TMX) had a good correlation with the daytime LST of Terra MODIS, with a Pearson’s correlation coefficient (R) of 0.92 and root mean square error (RMSE) of 4.8 °C, and the minimum SAT (TMN) showed a good correlation with the nighttime LST of Terra MODIS, with an R of 0.93 and RMSE of 4.2 °C. When analyzing temperature characteristics by land use (urban, paddy, upland crop, forest, grass, wetland, bare field, and water), it was confirmed that the climate mitigation effect of the wetland and vegetation area appeared in the LSTs and the observed SAT. In the cold wave period, the average temperatures for urban and wetland areas was the highest, and the average temperature for wetland and forest was not higher than that of other land use classes. As the SAT results predicted through the LSTM model, the accuracy of the TMN during the cold wave period was 0.59 for the coefficient of determination (R2), 3.1 °C for RMSE, and 0.76 for the index of agreement (IoA), while the accuracy of the TMX for the heat wave period was 0.24 for R2, 2.23 °C for RMSE, and 0.63 for IoA.
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14
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Hough I, Just AC, Zhou B, Dorman M, Lepeule J, Kloog I. A multi-resolution air temperature model for France from MODIS and Landsat thermal data. ENVIRONMENTAL RESEARCH 2020; 183:109244. [PMID: 32097815 PMCID: PMC7167357 DOI: 10.1016/j.envres.2020.109244] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 01/17/2020] [Accepted: 02/07/2020] [Indexed: 06/10/2023]
Abstract
Understanding and managing the health effects of ambient temperature (Ta) in a warming, urbanizing world requires spatially- and temporally-resolved Ta at high resolutions. This is challenging in a large area like France which includes highly variable topography, rural areas with few weather stations, and heterogeneous urban areas where Ta can vary at fine spatial scales. We have modeled daily Ta from 2000 to 2016 at a base resolution of 1 km2 across continental France and at a 200 × 200 m2 resolution over large urban areas. For each day we predict three Ta measures: minimum (Tmin), mean (Tmean), and maximum (Tmax). We start by using linear mixed models to calibrate daily Ta observations from weather stations with remotely sensed MODIS land surface temperature (LST) and other spatial predictors (e.g. NDVI, elevation) on a 1 km2 grid. We fill gaps where LST is missing (e.g. due to cloud cover) with additional mixed models that capture the relationship between predicted Ta at each location and observed Ta at nearby weather stations. The resulting 1 km Ta models perform very well, with ten-fold cross-validated R2 of 0.92, 0.97, and 0.95, mean absolute error (MAE) of 1.4 °C, 0.9 °C, and 1.4 °C, and root mean square error (RMSE) of 1.9 °C, 1.3 °C, and 1.8 °C (Tmin, Tmean, and Tmax, respectively) for the initial calibration stage. To increase the spatial resolution over large urban areas, we train random forest and extreme gradient boosting models to predict the residuals (R) of the 1 km Ta predictions on a 200 × 200 m2 grid. In this stage we replace MODIS LST and NDVI with composited top-of-atmosphere brightness temperature and NDVI from the Landsat 5, 7, and 8 satellites. We use a generalized additive model to ensemble the random forest and extreme gradient boosting predictions with weights that vary spatially and by the magnitude of the predicted residual. The 200 m models also perform well, with ten-fold cross-validated R2 of 0.79, 0.79, and 0.85, MAE of 0.4, 0.3, and 0.3, and RMSE of 0.6, 0.4, and 0.5 (Rmin, Rmean, and Rmax, respectively). Our model will reduce bias in epidemiological studies in France by improving Ta exposure assessment in both urban and rural areas, and our methodology demonstrates that MODIS and Landsat thermal data can be used to generate gap-free timeseries of daily minimum, maximum, and mean Ta at a 200 × 200 m2 spatial resolution.
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Affiliation(s)
- Ian Hough
- Univ. Grenoble Alpes, Inserm, CNRS, IAB, Site Sante, Allée des Alpes, 38700, La Tronche, France; Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Be'er Sheva, Israel.
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029-5674, USA
| | - Bin Zhou
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Be'er Sheva, Israel
| | - Michael Dorman
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Be'er Sheva, Israel
| | - Johanna Lepeule
- Univ. Grenoble Alpes, Inserm, CNRS, IAB, Site Sante, Allée des Alpes, 38700, La Tronche, France
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Be'er Sheva, Israel
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15
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Anwar MY, Warren JL, Pitzer VE. Diarrhea Patterns and Climate: A Spatiotemporal Bayesian Hierarchical Analysis of Diarrheal Disease in Afghanistan. Am J Trop Med Hyg 2020; 101:525-533. [PMID: 31392940 DOI: 10.4269/ajtmh.18-0735] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Subject to a high burden of diarrheal diseases, Afghanistan is also susceptible to climate change. This study investigated the spatiotemporal distribution of diarrheal disease in the country and how associated it is with climate variables. Using monthly aggregated new cases of acute diarrhea reported between 2010 and 2016 and monthly averaged climate data at the district level, we fitted a hierarchical Bayesian spatiotemporal statistical model. We found aridity and mean daily temperature were positively associated with diarrhea incidence; every 1°C increase in mean daily temperature and 0.01-unit change in the aridity index were associated with a 0.70% (CI: 0.67%, 0.73%) increase and a 4.79% (CI: 4.30%, 5.26%) increase in the risk of diarrhea, respectively. Average annual temperature, on the other hand, was negatively associated, with a 3.7% (CI: 3.74%, 3.68) decrease in risk for every degree Celsius increase in annual average temperature. Temporally, most districts exhibited similar seasonal trends, with incidence peaking in summer, except for the eastern region where differences in climate patterns and population density may be associated with high rates of diarrhea throughout the year. The results from this study highlight the significant role of climate in shaping diarrheal patterns in Afghanistan, allowing policymakers to account for potential impacts of climate change in their public health assessments.
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Affiliation(s)
- Mohammad Y Anwar
- Department of Epidemiology, University of Louisville School of Public Health and Information Sciences, Louisville, Kentucky
| | - Joshua L Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
| | - Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut
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16
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Comparison of temperature-mortality associations estimated with different exposure metrics. Environ Epidemiol 2019; 3:e072. [PMID: 33195965 PMCID: PMC7608890 DOI: 10.1097/ee9.0000000000000072] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 09/09/2019] [Indexed: 11/25/2022] Open
Abstract
Background: Studies of the short-term association between ambient temperature and mortality often use temperature observations from a single monitoring station, frequently located at the nearest airport, to represent the exposure of individuals living across large areas. Population-weighted temperature estimates constructed from gridded meteorological data may offer an opportunity to improve exposure assessment in locations where station observations do not fully capture the average exposure of the population of interest. Methods: We compared the association between daily mean temperature and mortality in each of 113 United States counties using (1) temperature observations from a single weather station and (2) population-weighted temperature estimates constructed from a gridded meteorological dataset. We used distributed lag nonlinear models to estimate the 21-day cumulative association between temperature and mortality in each county, 1987–2006, adjusting for seasonal and long-term trends, day of week, and holidays. Results: In the majority (73.4%) of counties, the relative risk of death on extremely hot days (99th percentile of weather station temperature) versus the minimum mortality temperature was larger when generated from the population-weighted estimates. In contrast, relative risks on extremely cold days (first percentile of weather station temperature) were often larger when generated from the weather station observations. In most counties, the difference in associations estimated from the two temperature metrics was small. Conclusions: In a large, multi-site analysis, temperature-mortality associations were largely similar when estimated from weather station observations versus population-weighted temperature estimates. However, spatially refined exposure data may be more appropriate for analyses seeking to elucidate local health effects.
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17
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Lima AO, Lyra GB, de Souza JL, Lyra GB, de Oliveira-Júnior JF, Santos AAR. Assessment of monthly global solar irradiation estimates using air temperature in different climates of the state of Rio de Janeiro, Southeastern Brazil. SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-1041-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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18
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Wei Y, Wang Y, Lin CK, Yin K, Yang J, Shi L, Li L, Zanobetti A, Schwartz JD. Associations between seasonal temperature and dementia-associated hospitalizations in New England. ENVIRONMENT INTERNATIONAL 2019; 126:228-233. [PMID: 30822651 PMCID: PMC8491247 DOI: 10.1016/j.envint.2018.12.054] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 12/17/2018] [Accepted: 12/24/2018] [Indexed: 05/22/2023]
Abstract
Human-induced climate change has accelerated in recent decades, causing adverse health effects. However, the impact of the changing climate on neurological disorders in the older population is not well understood. We applied time-varying Cox proportional hazards models to estimate the associations between hospital admissions for dementia and the mean and variability of summer and winter temperatures in New England. We estimated seasonal temperatures for each New England zip code using a satellite-based prediction model. By characterizing spatial differences and temporal fluctuations in seasonal temperatures, we observed a lower risk of dementia-associated hospital admissions in years when local temperatures in either summer (hazard ration [HR] = 0.98; 95% confidence interval [CI]: 0.96, 1.00) or winter (HR = 0.97; 95% CI: 0.94, 0.99) were higher than average, and a greater risk of dementia-associated admissions for older adults living in zip codes with higher temperature variations. Effect modifications by sex, race, age, and dual eligibility were considered to examine vulnerability of population subgroups. Our results suggest that cooler-than-average temperatures and higher temperature variability increase the risk of dementia-associated hospital admissions. Thus, climate change may affect progression of dementia and associated hospitalization costs.
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Affiliation(s)
- Yaguang Wei
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States of America.
| | - Yan Wang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States of America; Department of Biostatistics, Harvard T.H. Chan School of Public Health, United States of America
| | - Cheng-Kuan Lin
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States of America
| | - Kanhua Yin
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, United States of America
| | - Jiabei Yang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, United States of America
| | - Liuhua Shi
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States of America
| | - Longxiang Li
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States of America
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States of America
| | - Joel D Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States of America
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19
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O'Lenick CR, Wilhelmi OV, Michael R, Hayden MH, Baniassadi A, Wiedinmyer C, Monaghan AJ, Crank PJ, Sailor DJ. Urban heat and air pollution: A framework for integrating population vulnerability and indoor exposure in health risk analyses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 660:715-723. [PMID: 30743957 DOI: 10.1016/j.scitotenv.2019.01.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 12/15/2018] [Accepted: 01/01/2019] [Indexed: 05/07/2023]
Abstract
Urban growth and climate change will exacerbate extreme heat events and air pollution, posing considerable health challenges to urban populations. Although epidemiological studies have shown associations between health outcomes and exposures to ambient air pollution and extreme heat, the degree to which indoor exposures and social and behavioral factors may confound or modify these observed effects remains underexplored. To address this knowledge gap, we explore the linkages between vulnerability science and epidemiological conceptualizations of risk to propose a conceptual and analytical framework for characterizing current and future health risks to air pollution and extreme heat, indoors and outdoors. Our framework offers guidance for research on climatic variability, population vulnerability, the built environment, and health effects by illustrating how health data, spatially resolved ambient data, estimates of indoor conditions, and household-level vulnerability data can be integrated into an epidemiological model. We also describe an approach for characterizing population adaptive capacity and indoor exposure for use in population-based epidemiological models. Our framework and methods represent novel resources for the evaluation of health risks from extreme heat and air pollution, both indoors and outdoors.
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Affiliation(s)
- Cassandra R O'Lenick
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA.
| | - Olga V Wilhelmi
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Ryan Michael
- Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
| | - Mary H Hayden
- University of Colorado-Colorado Springs, Colorado Springs, CO, USA
| | - Amir Baniassadi
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USA
| | | | | | - Peter J Crank
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA
| | - David J Sailor
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA
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20
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Abstract
PURPOSE OF REVIEW Preterm birth is the leading cause of global child mortality, and survivors are at increased risk of multiple morbidities that can continue into adulthood. Recent studies have suggested that maternal exposure to air pollution and high and low ambient temperatures may increase the risk of preterm birth, whereas proximity to green space may decrease it. This review summarizes these findings and suggests avenues for further research. RECENT FINDINGS Particulate matter may be associated with an increased risk of preterm birth, but the magnitude of the effect remains unclear. Heat and cold likely increase the risk of preterm birth, with stronger evidence for heat. The first and third trimesters may be sensitive periods for exposure to both temperature and particulate matter, but the underlying biological mechanisms are incompletely understood. Context-appropriate green space can substantially reduce particulate matter levels and mitigate urban heat islands. SUMMARY In a warming, urbanizing world, exposure to unusual temperatures and elevated particulate matter levels represent an increasing risk for pregnant women. Green infrastructure might help mitigate this risk, but further research is needed to confirm its effects in complex urban environments and evaluate the contribution of both indoor and outdoor particulate matter and air temperature to personal exposure and preterm birth.
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Abstract
PURPOSE OF REVIEW Low, high, extreme, and variable temperatures have been linked to multiple adverse health outcomes, particularly among the elderly and children. Recent models incorporating satellite remote sensing data have mitigated several limitations of previous studies, improving exposure assessment. This review focuses on these new temperature exposure models and their application in epidemiological studies. RECENT FINDINGS Satellite observations of land surface temperature have been used to model air temperature across large spatial areas at high spatiotemporal resolutions. These models enable exposure assessment of entire populations and have been shown to reduce error in exposure estimates, thus mitigating downward bias in health effect estimates. SUMMARY Satellite-based models improve our understanding of spatiotemporal variation in temperature and the associated health effects. Further research should focus on improving the resolution of these models, especially in urban areas, and increasing their use in epidemiological studies of direct temperature exposure and vector-borne diseases.
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22
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Zhang S, Breitner S, Cascio WE, Devlin RB, Neas LM, Diaz-Sanchez D, Kraus WE, Schwartz J, Hauser ER, Peters A, Schneider A. Short-term effects of fine particulate matter and ozone on the cardiac conduction system in patients undergoing cardiac catheterization. Part Fibre Toxicol 2018; 15:38. [PMID: 30305173 PMCID: PMC6180522 DOI: 10.1186/s12989-018-0275-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 09/28/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Air pollution-induced changes in cardiac electrophysiological properties could be a pathway linking air pollution and cardiovascular events. The evidence of air pollution effects on the cardiac conduction system is incomplete yet. We investigated short-term effects of particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) and ozone (O3) on cardiac electrical impulse propagation and repolarization as recorded in surface electrocardiograms (ECG). METHODS We analyzed repeated 12-lead ECG measurements performed on 5,332 patients between 2001 and 2012. The participants came from the Duke CATHGEN Study who underwent cardiac catheterization and resided in North Carolina, United States (NC, U.S.). Daily concentrations of PM2.5 and O3 at each participant's home address were predicted with a hybrid air quality exposure model. We used generalized additive mixed models to investigate the associations of PM2.5 and O3 with the PR interval, QRS interval, heart rate-corrected QT interval (QTc), and heart rate (HR). The temporal lag structures of the associations were examined using distributed-lag models. RESULTS Elevated PM2.5 and O3 were associated with four-day lagged lengthening of the PR and QRS intervals, and with one-day lagged increases in HR. We observed immediate effects on the lengthening of the QTc interval for both PM2.5 and O3, as well as delayed effects for PM2.5 (lagged by 3 - 4 days). The associations of PM2.5 and O3 with the PR interval and the association of O3 with the QRS interval persisted until up to seven days after exposure. CONCLUSIONS In patients undergoing cardiac catheterization, short-term exposure to air pollution was associated with increased HR and delays in atrioventricular conduction, ventricular depolarization and repolarization.
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Affiliation(s)
- Siqi Zhang
- Institute of Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, P.O. Box 11 29, D-85764, Neuherberg, Germany.
| | - Susanne Breitner
- Institute of Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, P.O. Box 11 29, D-85764, Neuherberg, Germany
| | - Wayne E Cascio
- National Health and Environmental Effects Research Laboratory, US Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - Robert B Devlin
- National Health and Environmental Effects Research Laboratory, US Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - Lucas M Neas
- National Health and Environmental Effects Research Laboratory, US Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - David Diaz-Sanchez
- National Health and Environmental Effects Research Laboratory, US Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
| | - William E Kraus
- Duke Molecular Physiology Institute, School of Medicine, Duke University, Durham, NC, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Elizabeth R Hauser
- Duke Molecular Physiology Institute, School of Medicine, Duke University, Durham, NC, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, P.O. Box 11 29, D-85764, Neuherberg, Germany
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, P.O. Box 11 29, D-85764, Neuherberg, Germany
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23
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Lou H, Zhao C, Yang S, Shi L, Wang Y, Ren X, Bai J. Quantitative evaluation of legacy phosphorus and its spatial distribution. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2018; 211:296-305. [PMID: 29408079 DOI: 10.1016/j.jenvman.2018.01.062] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 06/07/2023]
Abstract
A phosphorus resource crisis threatens the security of global crop production, especially in developing countries like China and Brazil. Legacy phosphorus (legacy-P), which is left behind in agricultural soil by over-fertilization, can help address this issue as a new resource in the soil phosphorus pool. However, issues involved with calculating and defining the spatial distribution of legacy-P hinder its future utilization. To resolve these issues, this study applied remote sensing and ecohydrological modeling to precisely quantify legacy-P and define its spatial distribution in China's Sanjiang Plain from 2000 to 2014. The total legacy-P in the study area was calculated as 579,090 t with an annual average of 38,600 t; this comprises 51.83% of the phosphorus fertilizer applied annually. From 2000 to 2014, the annual amount of legacy-P increased by more than 3.42-fold, equivalent to a 2460-ton increase each year. The spatial distribution of legacy-P showed heterogeneity and agglomeration in this area, with peaks in cultivated land experiencing long-term agricultural development. This study supplies a new approach to finding legacy-P in soil as a precondition for future utilization. Once its spatial distribution is known, legacy-P can be better utilized in agriculture to help alleviate the phosphorus resource crisis.
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Affiliation(s)
- Hezhen Lou
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China
| | - Changsen Zhao
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China
| | - Shengtian Yang
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China.
| | - Liuhua Shi
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Landmark Center 404-M, 401 Park Drive, Boston, MA 02115, USA
| | - Yue Wang
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Xiaoyu Ren
- Beijing Meteorological Bureau, Beijing Weather Modification Office, Beijing, 100089, China
| | - Juan Bai
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China
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24
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Rosenfeld A, Dorman M, Schwartz J, Novack V, Just AC, Kloog I. Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel. ENVIRONMENTAL RESEARCH 2017; 159:297-312. [PMID: 28837902 DOI: 10.1016/j.envres.2017.08.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 08/07/2017] [Accepted: 08/08/2017] [Indexed: 05/21/2023]
Abstract
Meteorological stations measure air temperature (Ta) accurately with high temporal resolution, but usually suffer from limited spatial resolution due to their sparse distribution across rural, undeveloped or less populated areas. Remote sensing satellite-based measurements provide daily surface temperature (Ts) data in high spatial and temporal resolution and can improve the estimation of daily Ta. In this study we developed spatiotemporally resolved models which allow us to predict three daily parameters: Ta Max (day time), 24h mean, and Ta Min (night time) on a fine 1km grid across the state of Israel. We used and compared both the Aqua and Terra MODIS satellites. We used linear mixed effect models, IDW (inverse distance weighted) interpolations and thin plate splines (using a smooth nonparametric function of longitude and latitude) to first calibrate between Ts and Ta in those locations where we have available data for both and used that calibration to fill in neighboring cells without surface monitors or missing Ts. Out-of-sample ten-fold cross validation (CV) was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with and without available Ts observations for both Aqua and Terra (CV Aqua R2 results for min 0.966, mean 0.986, and max 0.967; CV Terra R2 results for min 0.965, mean 0.987, and max 0.968). Our research shows that daily min, mean and max Ta can be reliably predicted using daily MODIS Ts data even across Israel, with high accuracy even for days without Ta or Ts data. These predictions can be used as three separate Ta exposures in epidemiology studies for better diurnal exposure assessment.
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Affiliation(s)
- Adar Rosenfeld
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel
| | - Michael Dorman
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel
| | - Joel Schwartz
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Cambridge, MA, USA
| | - Victor Novack
- Clinical Research Center, Soroka University Medical Center, Beer Sheva, Israel
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel.
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25
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Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data. REMOTE SENSING 2017. [DOI: 10.3390/rs9050398] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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26
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Wang Y, Shi L, Lee M, Liu P, Di Q, Zanobetti A, Schwartz JD. Long-term Exposure to PM2.5 and Mortality Among Older Adults in the Southeastern US. Epidemiology 2017; 28:207-214. [PMID: 28005571 PMCID: PMC5285321 DOI: 10.1097/ede.0000000000000614] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Little is known about what factors modify the effect of long-term exposure to PM2.5 on mortality, in part because in most previous studies certain groups such as rural residents and individuals with lower socioeconomic status (SES) are under-represented. METHODS We studied 13.1 million Medicare beneficiaries (age ≥65) residing in seven southeastern US states during 2000-2013 with 95 million person-years of follow-up. We predicted annual average of PM2.5 in each zip code tabulation area (ZCTA) using a hybrid spatiotemporal model. We fit Cox proportional hazards models to estimate the association between long-term PM2.5 and mortality. We tested effect modification by individual-level covariates (race, sex, eligibility for both Medicare and Medicaid, and medical history), neighborhood-level covariates (urbanicity, percentage below poverty level, lower education, median income, and median home value), mean summer temperature, and mass fraction of 11 PM2.5 components. RESULTS The hazard ratio (HR) for death was 1.021 (95% confidence interval: 1.019, 1.022) per 1 μg m increase in annual PM2.5. The HR decreased with age. It was higher among males, non-whites, dual-eligible individuals, and beneficiaries with previous hospital admissions. It was higher in neighborhoods with lower SES or higher urbanicity. The HR increased with mean summer temperature. The risk associated with PM2.5 increased with relative concentration of elemental carbon, vanadium, copper, calcium, and iron and decreased with nitrate, organic carbon, and sulfate. CONCLUSIONS Associations between long-term PM2.5 exposure and death were modified by individual-level, neighborhood-level variables, temperature, and chemical compositions.
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Affiliation(s)
- Yan Wang
- Department of Environmental Health, Harvard T.H. Chan School of
Public Health
| | - Liuhua Shi
- Department of Environmental Health, Harvard T.H. Chan School of
Public Health
| | - Mihye Lee
- Department of Environmental Health, Harvard T.H. Chan School of
Public Health
| | - Pengfei Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard
University
| | - Qian Di
- Department of Environmental Health, Harvard T.H. Chan School of
Public Health
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard T.H. Chan School of
Public Health
| | - Joel D Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of
Public Health
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27
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Pelta R, Chudnovsky AA. Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 579:675-684. [PMID: 27889213 DOI: 10.1016/j.scitotenv.2016.11.042] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 11/02/2016] [Accepted: 11/06/2016] [Indexed: 05/17/2023]
Abstract
Although meteorological monitoring stations provide accurate measurements of Air Temperature (AT), their spatial coverage within a given region is limited and thus is often insufficient for exposure and epidemiological studies. In many applications, satellite imagery measures energy flux, which is spatially continuous, and calculates Brightness Temperature (BT) that used as an input parameter. Although both quantities (AT-BT) are physically related, the correlation between them is not straightforward, and varies daily due to parameters such as meteorological conditions, surface moisture, land use, satellite-surface geometry and others. In this paper we first investigate the relationship between AT and BT as measured by 39 meteorological stations in Israel during 1984-2015. Thereafter, we apply mixed regression models with daily random slopes to calibrate Landsat BT data with monitored AT measurements for the period 1984-2015. Results show that AT can be predicted with high accuracy by using BT with high spatial resolution. The model shows relatively high accuracy estimation of AT (R2=0.92, RMSE=1.58°C, slope=0.90). Incorporating meteorological parameters into the model generates better accuracy (R2=0.935) than the AT-BT model (R2=0.92). Furthermore, based on the relatively high model accuracy, we investigated the spatial patterns of AT within the study domain. In the latter we focused on July-August, as these two months are characterized by relativity stable synoptic conditions in the study area. In addition, a temporal change in AT during the last 30years was estimated and verified using available meteorological stations and two additional remote sensing platforms. Finally, the impact of different land coverage on AT were estimated, as an example of future application of the presented approach.
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Affiliation(s)
- Ran Pelta
- Tel-Aviv University, AIRO-Laboratory, Department of Geography and Human Environment, School of Geosciences, Israel.
| | - Alexandra A Chudnovsky
- Tel-Aviv University, AIRO-Laboratory, Department of Geography and Human Environment, School of Geosciences, Israel; Harvrad T.H.Chan School of Public Health, Department of Environmental Health, Boston, MA, USA.
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28
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Lou H, Yang S, Zhao C, Shi L, Wu L, Wang Y, Wang Z. Detecting and analyzing soil phosphorus loss associated with critical source areas using a remote sensing approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2016; 573:397-408. [PMID: 27572533 DOI: 10.1016/j.scitotenv.2016.08.048] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 08/03/2016] [Accepted: 08/06/2016] [Indexed: 06/06/2023]
Abstract
The detection of critical source areas (CSAs) is a key step in managing soil phosphorus (P) loss and preventing the long-term eutrophication of water bodies at regional scale. Most related studies, however, focus on a local scale, which prevents a clear understanding of the spatial distribution of CSAs for soil P loss at regional scale. Moreover, the continual, long-term variation in CSAs was scarcely reported. It is impossible to identify the factors driving the variation in CSAs, or to collect land surface information essential for CSAs detection, by merely using the conventional methodologies at regional scale. This study proposes a new regional-scale approach, based on three satellite sensors (ASTER, TM/ETM and MODIS), that were implemented successfully to detect CSAs at regional scale over 15years (2000-2014). The approach incorporated five factors (precipitation, slope, soil erosion, land use, soil total phosphorus) that drive soil P loss from CSAs. Results show that the average area of critical phosphorus source areas (CPSAs) was 15,056km2 over the 15-year period, and it occupied 13.8% of the total area, with a range varying from 1.2% to 23.0%, in a representative, intensive agricultural area of China. In contrast to previous studies, we found that the locations of CSAs with P loss are spatially variable, and are more dispersed in their distribution over the long term. We also found that precipitation acts as a key driving factor in the variation of CSAs at regional scale. The regional-scale method can provide scientific guidance for managing soil phosphorus loss and preventing the long-term eutrophication of water bodies at regional scale, and shows great potential for exploring factors that drive the variation in CSAs at global scale.
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Affiliation(s)
- Hezhen Lou
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875,China
| | - Shengtian Yang
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875,China
| | - Changsen Zhao
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875,China.
| | - Liuhua Shi
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Landmark Center 404-M, 401 Park Drive, Boston, MA 02215, USA
| | - Linna Wu
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875,China; College of Resource and Environment Engineering, Guizhou University, Guizhou, Guiyang 550025, China
| | - Yue Wang
- Department of Geography, University of Wisconsin, Madison, WI 53705, USA
| | - Zhiwei Wang
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875,China
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29
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Estimating Daily Maximum and Minimum Land Air Surface Temperature Using MODIS Land Surface Temperature Data and Ground Truth Data in Northern Vietnam. REMOTE SENSING 2016. [DOI: 10.3390/rs8121002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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30
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Urban Heat Island Intensification during Hot Spells—The Case of Paris during the Summer of 2003. URBAN SCIENCE 2016. [DOI: 10.3390/urbansci1010003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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31
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Lee M, Shi L, Zanobetti A, Schwartz JD. Study on the association between ambient temperature and mortality using spatially resolved exposure data. ENVIRONMENTAL RESEARCH 2016; 151:610-617. [PMID: 27611992 PMCID: PMC5071163 DOI: 10.1016/j.envres.2016.08.029] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Revised: 08/25/2016] [Accepted: 08/25/2016] [Indexed: 05/03/2023]
Abstract
There are many studies that have posited an association between extreme temperature and increased mortality. However, most studies use temperature at a single station per city as the reference point to analyze deaths. This leads to exposure misclassification and usually the exclusion of exurban, small town, and rural populations. In addition, few studies control for confounding by PM2.5, which is expected to induce upward bias. The high-resolution temperature and PM2.5 data at a resolution of 1km2 were derived from satellite images and other land use sources. To capture the nonlinear association of temperature with mortality we fit a piecewise linear spline function for temperature, with a change in slope at -1°C and 28°C, the temperature threshold at which mortality in Georgia, North Carolina, and South Carolina increases due to cold and heat, respectively. We conducted stratified analyses by age group, sex, race, education, and urban vs nonurban, as well as sensitivity analyses of different temperature threshold and covariate sets. We found a 0.19% (95% CI=-0.98, 1.34%) increase in mortality for each 1°C decrease in temperature below -1°C and a 2.05% (95% CI=0.87, 3.24%) increase in mortality for each 1°C increase in temperature above 28°C, a 79.8% larger effect size for heat compared to the station-based metric. The effect estimates relying on the monitoring stations were 0.09% (95% CI=-0.79, 0.95%) and 1.14% (95% CI=0.08, 1.57%) for the equivalent temperature changes. The estimates were not confounded by PM2.5. Children under 15 years of age had the largest percentage increase per 1°C increase in temperature (8.19%, 95% CI=-0.38 to 17.49%) followed by Blacks (4.35%, 95% CI=2.22 to 6.53%). Higher education was a protective factor for the effect of extreme temperature on mortality. There was a suggestion that people in less urban areas were more susceptible to extreme temperature. The relationship between temperature and mortality was stronger when using exposure data with more spatial variability than using exposure data based on existing monitors alone.
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Affiliation(s)
- Mihye Lee
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
| | - Liuhua Shi
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Joel D Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
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33
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Wang Y, Bobb JF, Papi B, Wang Y, Kosheleva A, Di Q, Schwartz JD, Dominici F. Heat stroke admissions during heat waves in 1,916 US counties for the period from 1999 to 2010 and their effect modifiers. Environ Health 2016; 15:83. [PMID: 27503399 PMCID: PMC4977899 DOI: 10.1186/s12940-016-0167-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Accepted: 07/27/2016] [Indexed: 05/16/2023]
Abstract
BACKGROUND Heat stroke is a serious heat-related illness, especially among older adults. However, little is known regarding the spatiotemporal variation of heat stroke admissions during heat waves and what factors modify the adverse effects. METHODS We conducted a large-scale national study among 23.5 million Medicare fee-for-service beneficiaries per year residing in 1,916 US counties during 1999-2010. Heat wave days, defined as a period of at least two consecutive days with temperatures exceeding the 97th percentile of that county's temperatures, were matched to non-heat wave days by county and week. We fitted random-effects Poisson regression models to estimate the relative risk (RR) of heat stroke admissions on a heat wave day as compared to a matched non-heat wave day. A variety of effect modifiers were tested including individual-level covariates, community-level covariates, meteorological conditions, and the intensity and duration of the heat wave event. RESULTS The RR declined substantially from 71.0 (21.3-236.2) in 1999 to 3.5 (1.9-6.5) in 2010, and was highest in the northeast and lowest in the west north central regions of the US. We found a lower RR among counties with higher central air conditioning (AC) prevalence. More severe and longer-lasting heat waves had higher RRs. CONCLUSIONS Heat stroke hospitalizations associated with heat waves declined dramatically over time, indicating increased resilience to extreme heat among older adults. Considerable risks, however, still remain through 2010, which could be addressed through public health interventions at a regional scale to further increase central AC and monitoring heat waves.
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Affiliation(s)
- Yan Wang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215 USA
| | - Jennifer F. Bobb
- Biostatistics Unit, Group Health Research Institute, 1730 Minor Ave #1600, Seattle, WA 98101 USA
| | - Bianca Papi
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115 USA
| | - Yun Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115 USA
| | - Anna Kosheleva
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215 USA
| | - Qian Di
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215 USA
| | - Joel D. Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215 USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115 USA
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34
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Chronic effects of temperature on mortality in the Southeastern USA using satellite-based exposure metrics. Sci Rep 2016; 6:30161. [PMID: 27436237 PMCID: PMC4951799 DOI: 10.1038/srep30161] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 06/28/2016] [Indexed: 11/29/2022] Open
Abstract
Climate change may affect human health, particularly for elderly individuals who are vulnerable to temperature changes. While many studies have investigated the acute effects of heat, only a few have dealt with the chronic ones. We have examined the effects of seasonal temperatures on survival of the elderly in the Southeastern USA, where a large fraction of subpopulation resides. We found that both seasonal mean temperature and its standard deviation (SD) affected long-term survival among the 13 million Medicare beneficiaries (aged 65+) in this region during 2000–2013. A 1 °C increase in summer mean temperature corresponded to an increase of 2.5% in death rate. Whereas, 1 °C increase in winter mean temperature was associated with a decrease of 1.5%. Increases in seasonal temperature SD also influence mortality. We decomposed seasonal mean temperature and its temperature SD into long-term geographic contrasts between ZIP codes and annual anomalies within ZIP code. Effect modifications by different subgroups were also examined to find out whether certain individuals are more vulnerable. Our findings will be critical to future efforts assessing health risks related to the future climate change.
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35
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Xu Z, Yin X, Yang Z, Cai Y, Sun T. New model to assessing nutrient assimilative capacity in plant-dominated lakes: Considering ecological effects of hydrological changes. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2016.03.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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36
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An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas. REMOTE SENSING 2016. [DOI: 10.3390/rs8040306] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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