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Land use regression LUR

Li Chen, Shuang Gao, Hui Zhang, Yanling Sun, Zhenxing Ma, Sverre Vedal, Jian Mao, Zhipeng Bai
Concentrations of particulate matter with aerodynamic diameter <2.5 μm (PM2.5 ) are relatively high in China. Estimation of PM2.5 exposure is complex because PM2.5 exhibits complex spatiotemporal patterns. To improve the validity of exposure predictions, several methods have been developed and applied worldwide. A hybrid approach combining a land use regression (LUR) model and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals were developed to estimate the PM2.5 concentrations on a national scale in China...
May 3, 2018: Environment International
Saori Kashima, Takashi Yorifuji, Norie Sawada, Tomoki Nakaya, Akira Eboshida
BACKGROUND: Typically, land use regression (LUR) models have been developed using campaign monitoring data rather than routine monitoring data. However, the latter have advantages such as low cost and long-term coverage. Based on the idea that LUR models representing regional differences in air pollution and regional road structures are optimal, the objective of this study was to evaluate the validity of LUR models for nitrogen dioxide (NO2 ) based on routine and campaign monitoring data obtained from an urban area...
August 1, 2018: Science of the Total Environment
Wei Ouyang, Bing Gao, Hongguang Cheng, Zengchao Hao, Ni Wu
Fine particulate matter (PM2.5 ) pollution exposure has an adverse impact on public health, and some vulnerable social groups suffer from unfair exposure. Few studies have been conducted to estimate and to compare the exposure and inequality of different residential demographics at multiple time scales. This study assessed the exposures level of age and education subgroups on the whole city and the exposure inequalities of these subgroups within a concentration interval area for PM2.5 pollution at multiple time scales in Beijing in 2015...
April 20, 2018: Science of the Total Environment
Margaux Sanchez, Albert Ambros, Carles Milà, Maëlle Salmon, Kalpana Balakrishnan, Sankar Sambandam, V Sreekanth, Julian D Marshall, Cathryn Tonne
Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e...
April 4, 2018: Science of the Total Environment
Haneen Khreis, Kees de Hoogh, Mark J Nieuwenhuijsen
BACKGROUND: Asthma is the most common chronic disease in children. Traffic-related air pollution (TRAP) may be an important exposure contributing to its development. In the UK, Bradford is a deprived city suffering from childhood asthma rates higher than national and regional averages and TRAP is of particular concern to the local communities. AIMS: We estimated the burden of childhood asthma attributable to air pollution and specifically TRAP in Bradford. Air pollution exposures were estimated using a newly developed full-chain exposure assessment model and an existing land-use regression model (LUR)...
March 24, 2018: Environment International
O Naughton, A Donnelly, P Nolan, F Pilla, B D Misstear, B Broderick
Estimating pollutant concentrations at a local and regional scale is essential in environmental and health policy decision making. Here we present a novel land use regression (LUR) modelling methodology that exploits the high temporal resolution of fixed-site monitoring (FSM) to produce a national-scale air quality model for the key pollutant NO2 . The methodology partitions concentration time series from a national FSM network into wind-dependent sectors or "wedges". A LUR model is derived using predictor variables calculated within the directional wind sectors, and compared against the long-term average concentrations within each sector...
March 2, 2018: Science of the Total Environment
Y J Zhang, D H Zhou, Z P Bai, F X Xue
Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed...
February 10, 2018: Zhonghua Liu Xing Bing Xue za Zhi, Zhonghua Liuxingbingxue Zazhi
Laura Minet, Rick Liu, Marie-France Valois, Junshi Xu, Scott Weichenthal, Marianne Hatzopoulou
Land-use regression (LUR) models of air pollutants are frequently developed on the basis of short-term stationary or mobile monitoring approaches, which raises the question of whether these two data collection protocols lead to similar exposure surfaces. In this study, we measured ultrafine particles (UFP) and black carbon (BC) concentrations in Toronto during summer 2016, using two short-term data collection approaches: mobile, involving 3023 road segments sampled on bicycles, and stationary, involving 92 sidewalk locations...
March 20, 2018: Environmental Science & Technology
Luke D Knibbs, Craig P Coorey, Matthew J Bechle, Julian D Marshall, Michael G Hewson, Bin Jalaludin, Geoff G Morgan, Adrian G Barnett
Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004)...
May 2018: Environmental Research
Myrna M T de Rooij, Dick J J Heederik, Erik J H M van Nunen, Isabella J van Schothorst, Catharina B M Maassen, Gerard Hoek, Inge M Wouters
BACKGROUND: Results from studies on residential health effects of livestock farming are inconsistent, potentially due to simple exposure proxies used (e.g., livestock density). Accuracy of these proxies compared with measured exposure concentrations is unknown. OBJECTIVES: We aimed to assess spatial variation of endotoxin in PM10 (particulate matter ≤10μm) at residential level in a livestock-dense area, compare simple livestock exposure proxies to measured endotoxin concentrations, and evaluate whether land-use regression (LUR) can be used to explain spatial variation of endotoxin...
January 11, 2018: Environmental Health Perspectives
Jing Yang, Yi Yang, Rui-Shan Chen, Xiang-Zhou Meng, Jie Xu, Abdul Qadeer, Min Liu
To explore the influence of rapid urbanization development on the accumulation of 16 priority PAHs in urban environment, thirty-three surface sediments from city lakes in different urbanized areas of Shanghai were collected to evaluate the occurrence characteristic and source apportionment of PAHs. The concentrations of Σ16 PAHs in lake surface sediments ranged from 55.7 to 4928 ng g-1 with a mean value of 1131 ng g-1 (standard deviation, 1228 ng g-1 ), of which 4-, 5- and 6-ring PAHs were the dominant components...
April 2018: Environmental Pollution
Shi V Liu, Fu-Lin Chen, Jianping Xue
An important factor in evaluating health risk of near-road air pollution is to accurately estimate the traffic-related vehicle emission of air pollutants. Inclusion of traffic parameters such as road length/area, distance to roads, and traffic volume/intensity into models such as land use regression (LUR) models has improved exposure estimation. To better understand the relationship between vehicle emissions and near-road air pollution, we evaluated three traffic density-based indices: Major-Road Density (MRD), All-Traffic Density (ATD) and Heavy-Traffic Density (HTD) which represent the proportions of major roads, major road with annual average daily traffic (AADT), and major road with commercial annual average daily traffic (CAADT) in a buffered area, respectively...
December 15, 2017: International Journal of Environmental Research and Public Health
Hugh Z Li, Timothy R Dallmann, Xiang Li, Peishi Gu, Albert A Presto
We conducted a mobile sampling campaign in a historically industrialized terrain (Pittsburgh, PA) targeting spatial heterogeneity of organic aerosol. Thirty-six sampling sites were chosen based on stratification of traffic, industrial source density, and elevation. We collected organic carbon (OC) on quartz filters, quantified different OC components with thermal-optical analysis, and grouped them based on volatility in decreasing order (OC1, OC2, OC3, OC4, and pyrolyzed carbon (PC)). We compared our ambient OC concentrations (both gas and particle phase) to similar measurements from vehicle dynamometer tests, cooking emissions, biomass burning emissions, and a highway traffic tunnel...
January 16, 2018: Environmental Science & Technology
Bin Han, Li-Wen Hu, Zhipeng Bai
Assessment of human exposure to air pollution is a fundamental part of the more general process of health risk assessment. The measurement methods for exposure assessment now include personal exposure monitoring, indoor-outdoor sampling, mobile monitoring, and exposure assessment modeling (such as proximity models, interpolation model, air dispersion models, and land-use regression (LUR) models). Among these methods, personal exposure measurement is considered to be the most accurate method of pollutant exposure assessment until now, since it can better quantify observed differences and better reflect exposure among smaller groups of people at ground level...
2017: Advances in Experimental Medicine and Biology
Georgia Miskell, Jennifer A Salmond, David E Williams
Portable low-cost instruments have been validated and used to measure ambient nitrogen dioxide (NO2 ) at multiple sites over a small urban area with 20min time resolution. We use these results combined with land use regression (LUR) and rank correlation methods to explore the effects of traffic, urban design features, and local meteorology and atmosphere chemistry on small-scale spatio-temporal variations. We measured NO2 at 45 sites around the downtown area of Vancouver, BC, in spring 2016, and constructed four different models: i) a model based on averaging concentrations observed at each site over the whole measurement period, and separate temporal models for ii) morning, iii) midday, and iv) afternoon...
April 1, 2018: Science of the Total Environment
L F Weissert, J A Salmond, G Miskell, M Alavi-Shoshtari, D E Williams
Land use regression (LUR) analysis has become a key method to explain air pollutant concentrations at unmeasured sites at city or country scales, but little is known about the applicability of LUR at microscales. We present a microscale LUR model developed for a heavy trafficked section of road in Auckland, New Zealand. We also test the within-city transferability of LUR models developed at different spatial scales (local scale and city scale). Nitrogen dioxide (NO2 ) was measured during summer at 40 sites and a LUR model was developed based on standard criteria...
April 1, 2018: Science of the Total Environment
Yuan Shi, Lutz Katzschner, Edward Ng
Urban heat island (UHI) effect significantly raises the health burden and building energy consumption in the high-density urban environment of Hong Kong. A better understanding of the spatiotemporal pattern of UHI is essential to health risk assessments and energy consumption management but challenging in a high-density environment due to the sparsely distributed meteorological stations and the highly diverse urban features. In this study, we modelled the spatiotemporal pattern of UHI effect using the land use regression (LUR) approach in geographic information system with meteorological records of the recent 4years (2013-2016), sounding data and geographic predictors in Hong Kong...
October 30, 2017: Science of the Total Environment
Chloé Sieber, Martina S Ragettli, Mark Brink, Olaniyan Toyib, Roslyn Baatjies, Apolline Saucy, Nicole Probst-Hensch, Mohamed Aqiel Dalvie, Martin Röösli
In low- and middle-income countries, noise exposure and its negative health effects have been little explored. The present study aimed to assess the noise exposure situation in adults living in informal settings in the Western Cape Province, South Africa. We conducted continuous one-week outdoor noise measurements at 134 homes in four different areas. These data were used to develop a land use regression (LUR) model to predict A-weighted day-evening-night equivalent sound levels (Lden ) from geographic information system (GIS) variables...
October 20, 2017: International Journal of Environmental Research and Public Health
John Gulliver, David Morley, Chrissi Dunster, Adrienne McCrea, Erik van Nunen, Ming-Yi Tsai, Nicoltae Probst-Hensch, Marloes Eeftens, Medea Imboden, Regina Ducret-Stich, Alessio Naccarati, Claudia Galassi, Andrea Ranzi, Mark Nieuwenhuijsen, Ariadna Curto, David Donaire-Gonzalez, Marta Cirach, Roel Vermeulen, Paolo Vineis, Gerard Hoek, Frank J Kelly
Oxidative potential (OP) of particulate matter (PM) is proposed as a biologically-relevant exposure metric for studies of air pollution and health. We aimed to evaluate the spatial variability of the OP of measured PM2.5 using ascorbate (AA) and (reduced) glutathione (GSH), and develop land use regression (LUR) models to explain this spatial variability. We estimated annual average values (m-3 ) of OPAA and OPGSH for five areas (Basel, CH; Catalonia, ES; London-Oxford, UK (no OPGSH ); the Netherlands; and Turin, IT) using PM2...
January 2018: Environmental Research
Cole Brokamp, Roman Jandarov, M B Rao, Grace LeMasters, Patrick Ryan
Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models...
February 2017: Atmospheric Environment
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