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Land use regression LUR

Luke David Knibbs, Craig P Coorey, Matthew John Bechle, Christine Therese Cowie, Mila Dirgawati, Jane S Heyworth, Guy Barrington Marks, Julian D Marshall, Lidia Morawska, Gavin Pereira, Michael G Hewson
Including satellite observations of nitrogen dioxide (NO2) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO2 samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO2¬ estimates from existing national satellite-based LUR models (developed with 68 regulatory monitors). The samplers spanned roadside, urban near traffic (≤100 m to a major road), and urban background (>100 m to a major road) locations...
October 21, 2016: Environmental Science & Technology
Ya-Ru Yang, Yung-Ming Chen, Szu-Ying Chen, Chang-Chuan Chan
BACKGROUND: This study aimed to investigate the associations between particulate matter (PM) exposures and renal function among adults. METHODS: We recruited 21,656 adults as subjects from 2007-2009. The Taiwanese Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to derive the estimated glomerular filtration rate (eGFR). Subjects with an eGFR lower than 60 mL/min/1.73 m(2) were defined as having chronic kidney disease (CKD). Land use regression (LUR) models were used to estimate individual exposures to PM with an aerodynamic diameter <10 μm (PM10), coarse particles (PMCoarse), fine particles (PM2...
October 7, 2016: Environmental Health Perspectives
M Cordioli, C Pironi, E De Munari, N Marmiroli, P Lauriola, A Ranzi
The epidemiological research benefits from an accurate characterization of both spatial and temporal variability of exposure to air pollution. This work aims at proposing a method to combine the high spatial resolution of Land Use Regression (LUR) models with the high temporal resolution of fixed site monitoring data, to model spatiotemporal variability of NO2 over a wide geographical area in Northern Italy. We developed seasonal LUR models to reconstruct the spatial distribution of a scaling factor that relates local concentrations to those measured at two reference central sites, one for the northern flat area and one for the southern mountain area...
September 24, 2016: Science of the Total Environment
Karin Fehsel, Tamara Schikowski, Michaela Jänner, Anke Hüls, Mohammed Voussoughi, Thomas Schulte, Andrea Vierkötter, Tom Teichert, Christian Herder, Dorothea Sugiri, Ursula Krämer, Christian Luckhaus
Genetic and environmental risk factors contribute to the pathogenesis of Alzheimer's dementia. Besides known genetic risk factors like the apolipoprotein (APO) Eε4 allele, single nuclear polymorphisms (SNPs) of the estrogen receptors (ESRs) are candidate genetic risk factors, while air pollution represents an environmental risk factor for dementia. Effects of these risk factors and their interaction were investigated in the SALIA cohort of 834 non-demented elderly women. Cognitive function was assessed by the CERAD-plus test battery...
September 14, 2016: Journal of Neural Transmission
Heresh Amini, Seyed-Mahmood Taghavi-Shahri, Sarah B Henderson, Vahid Hosseini, Hossein Hassankhany, Maryam Naderi, Solmaz Ahadi, Christian Schindler, Nino Künzli, Masud Yunesian
Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R(2) values for the LUR models ranged from 0.69 to 0.78 for NO, 0...
2016: Scientific Reports
Jonathan Gillespie, Iain J Beverland, Scott Hamilton, Sandosh Padmanabhan
We used a network of 135 NO2 passive diffusion tube sites to develop land use regression (LUR) models in a UK conurbation. Network sites were divided into four groups (32-35 sites per group) and models developed using combinations of 1-3 groups of "training" sites to evaluate how the number of training sites influenced model performance and residential NO2 exposure estimates for a cohort of 13 679 participants. All models explained moderate to high variance in training and independent "hold-out" data (Training adj...
October 18, 2016: Environmental Science & Technology
Brett J Tunno, Jessie L C Shmool, Drew R Michanowicz, Sheila Tripathy, Lauren G Chubb, Ellen Kinnee, Leah Cambal, Courtney Roper, Jane E Clougherty
Capturing intra-urban variation in diesel-related pollution exposures remains a challenge, given its complex chemical mix, and relatively few well-characterized ambient-air tracers for the multiple diesel sources in densely-populated urban areas. To capture fine-scale spatial resolution (50×50m grid cells) in diesel-related pollution, we used geographic information systems (GIS) to systematically allocate 36 sampling sites across downtown Pittsburgh, PA, USA (2.8km(2)), cross-stratifying to disentangle source impacts (i...
August 18, 2016: Science of the Total Environment
Stéphane Perron, Céline Plante, Martina S Ragettli, David J Kaiser, Sophie Goudreau, Audrey Smargiassi
The objective of our study was to measure the impact of transportation-related noise and total environmental noise on sleep disturbance for the residents of Montreal, Canada. A telephone-based survey on noise-related sleep disturbance among 4336 persons aged 18 years and over was conducted. LNight for each study participant was estimated using a land use regression (LUR) model. Distance of the respondent's residence to the nearest transportation noise source was also used as an indicator of noise exposure. The proportion of the population whose sleep was disturbed by outdoor environmental noise in the past 4 weeks was 12...
2016: International Journal of Environmental Research and Public Health
Mehdi Mokhtari, Mohammad Miri, Ali Nikoonahad, Ali Jalilian, Razi Naserifar, Hamid Reza Ghaffari, Farogh Kazembeigi
The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software...
November 2016: Acta Tropica
Michal Korek, Christer Johansson, Nina Svensson, Tomas Lind, Rob Beelen, Gerard Hoek, Göran Pershagen, Tom Bellander
Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM-LUR model using 93 biweekly observations of NOx at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NOx...
August 3, 2016: Journal of Exposure Science & Environmental Epidemiology
Kees de Hoogh, John Gulliver, Aaron van Donkelaar, Randall V Martin, Julian D Marshall, Matthew J Bechle, Giulia Cesaroni, Marta Cirach Pradas, Audrius Dedele, Marloes Eeftens, Bertil Forsberg, Claudia Galassi, Joachim Heinrich, Barbara Hoffmann, Bénédicte Jacquemin, Klea Katsouyanni, Michal Korek, Nino Künzli, Sarah J Lindley, Johanna Lepeule, Frederik Meleux, Audrey de Nazelle, Mark Nieuwenhuijsen, Wenche Nystad, Ole Raaschou-Nielsen, Annette Peters, Vincent-Henri Peuch, Laurence Rouil, Orsolya Udvardy, Rémy Slama, Morgane Stempfelet, Euripides G Stephanou, Ming Y Tsai, Tarja Yli-Tuomi, Gudrun Weinmayr, Bert Brunekreef, Danielle Vienneau, Gerard Hoek
Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network...
July 19, 2016: Environmental Research
Yuan Shi, Kevin Ka-Lun Lau, Edward Ng
Monitoring street-level particulates is essential to air quality management but challenging in high-density Hong Kong due to limitations in local monitoring network and the complexities of street environment. By employing vehicle-based mobile measurements, land use regression (LUR) models were developed to estimate the spatial variation of PM2.5 and PM10 in the downtown area of Hong Kong. Sampling runs were conducted along routes measuring a total of 30 km during a selected measurement period of total 14 days...
August 2, 2016: Environmental Science & Technology
Jian-sheng Wu, Wu-dan Xie, Jia-cheng Li
With the rapid development of urbanization, industrialization and motorization, air pollution has become one of the most serious environmental problems in our country, which has negative impacts on public health and ecological environment. LUR model is one of the common methods simulating spatial-temporal differentiation of air pollution at city scale. It has broad application in Europe and North America, but not really in China. Based on many studies at home and abroad, this study started with the main steps to develop LUR model, including obtaining the monitoring data, generating variables, developing models, model validation and regression mapping...
February 15, 2016: Huan Jing Ke Xue= Huanjing Kexue
Chang-Fu Wu, Fu-Hui Shen, Ya-Ru Li, Tsung-Ming Tsao, Ming-Jer Tsai, Chu-Chih Chen, Jing-Shiang Hwang, Sandy Huey-Jen Hsu, Hsing Chao, Kai-Jen Chuang, Charles C K Chou, Ya-Nan Wang, Chi-Chang Ho, Ta-Chen Su
This study evaluated whether exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) is associated with cardiovascular effects by examining a panel of 89 healthy subjects in Taipei, Taiwan. The subjects received two health examinations approximately 8months apart in 2013. Brachial-ankle pulse wave velocity (baPWV), a physiological indicator of arterial stiffness, and high-sensitivity C-reactive protein (hsCRP), a biomarker of vascular inflammations, were measured during each examination. Two exposure assessment methods were used for estimating the subjects' exposure to PM2...
November 1, 2016: Science of the Total Environment
Kazuhiko Ito, Sarah Johnson, Iyad Kheirbek, Jane Clougherty, Grant Pezeshki, Zev Ross, Holger Eisl, Thomas D Matte
Few past studies have collected and analyzed within-city variation of fine particulate matter (PM2.5) elements. We developed land-use regression (LUR) models to characterize spatial variation of 15 PM2.5 elements collected at 150 street-level locations in New York City during December 2008-November 2009: aluminum, bromine, calcium, copper, iron, potassium, manganese, sodium, nickel, lead, sulfur, silicon, titanium, vanadium, and zinc. Summer- and winter-only data available at 99 locations in the subsequent 3 years, up to November 2012, were analyzed to examine variation of LUR results across years...
July 19, 2016: Environmental Science & Technology
Chao Liu, Barron H Henderson, Dongfang Wang, Xinyuan Yang, Zhong-Ren Peng
Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM2.5 and NO2 concentrations at 35-45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables...
September 15, 2016: Science of the Total Environment
John Gulliver, Kees de Hoogh, Gerard Hoek, Danielle Vienneau, Daniela Fecht, Anna Hansell
Robust methods to estimate historic population air pollution exposures are important tools for epidemiological studies evaluating long-term health effects. We developed land use regression (LUR) models for NO2 exposure in Great Britain for 1991 and explored whether the choice of year-specific or back-extrapolated LUR yields 1) similar LUR variables and model performance, and 2) similar national and regional address-level and small-area concentrations. We constructed two LUR models for 1991using NO2 concentrations from the diffusion tube monitoring network, one using 75% of all available measurement sites (that over-represent industrial areas), and the other using 75% of a subset of sites proportionate to population by region to study the effects of monitoring site selection bias...
July 2016: Environment International
Marloes Eeftens, Reto Meier, Christian Schindler, Inmaculada Aguilera, Harish Phuleria, Alex Ineichen, Mark Davey, Regina Ducret-Stich, Dirk Keidel, Nicole Probst-Hensch, Nino Künzli, Ming-Yi Tsai
BACKGROUND: Land Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models. METHODS: Air pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution...
2016: Environmental Health: a Global Access Science Source
Meng Wang, Paul D Sampson, Jianlin Hu, Michael Kleeman, Joshua P Keller, Casey Olives, Adam A Szpiro, Sverre Vedal, Joel D Kaufman
Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin...
May 17, 2016: Environmental Science & Technology
Wei Xu, Erin A Riley, Elena Austin, Miyoko Sasakura, Lanae Schaal, Timothy R Gould, Kris Hartin, Christopher D Simpson, Paul D Sampson, Michael G Yost, Timothy V Larson, Guangli Xiu, Sverre Vedal
Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012...
March 23, 2016: Journal of Exposure Science & Environmental Epidemiology
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