ARTICLE pubs.acs.org/est
Mapping Urban Environmental Noise: A Land Use Regression Method Dan Xie,† Yi Liu,*,† and Jining Chen† †
School of Environment, Tsinghua University, Beijing 100084, China
bS Supporting Information ABSTRACT: Forecasting and preventing urban noise pollution are major challenges in urban environmental management. Most existing efforts, including experiment-based models, statistical models, and noise mapping, however, have limited capacity to explain the association between urban growth and corresponding noise change. Therefore, these conventional methods can hardly forecast urban noise at a given outlook of development layout. This paper, for the first time, introduces a land use regression method, which has been applied for simulating urban air quality for a decade, to construct an urban noise model (LUNOS) in Dalian Municipality, Northwest China. The LUNOS model describes noise as a dependent variable of surrounding various land areas via a regressive function. The results suggest that a linear model performs better in fitting monitoring data, and there is no significant difference of the LUNOS’s outputs when applied to different spatial scales. As the LUNOS facilitates a better understanding of the association between land use and urban environmental noise in comparison to conventional methods, it can be regarded as a promising tool for noise prediction for planning purposes and aid smart decision-making.
1. INTRODUCTION Forecasting and preventing urban noise pollution are major challenges in urban environmental management. It is of particular importance to relate the potential change of spatial distribution of noise to future urban expansion at planning stage. Modeling urban noise, however, has received pretty less appreciation in environmental forecasting and impact assessment. This is partially because the mechanisms of noise generation, propagation, and attenuation are relatively explicit compared to complicated dynamics of water and air pollutants in the environments. Nevertheless, difficulty in mapping environmental noise at a city-region level, in particular in forecasting urban noise change for an urban developing layout, still remains. Current efforts on assessing environmental noise mainly include experiment-based approaches, statistical models, and noise mapping. The experiment-based models are usually based on the physical theory of noise propagation and attenuation, and most current literature focuses on specific sources of noise such as transportation, industry, construction, and other social sources. Traffic noise, among others, is most extensively studied in these experimental studies. Commonly applied traffic noise models, involving FHWA, MITHRA, CORTN, etc., often assume point or line sources, uniform intervening terrain between traffic and receivers, and incorporate a similar propagation section.15 Experiment-based models could achieve high prediction accuracies. But to ensure this, these models require a large amount of data to calculate complex acoustic phenomena like multireflections, diffractions, and multiple absorptions. Also, these models are usually confined within a relatively small spatial scale. r 2011 American Chemical Society
According to the uncertainty mathematics principle, some statistical models are proposed to describe urban noise. Compared to experiment-based noise modeling, these kinds of models apply some simplified or gray procedures to deal with scarce data. Artificial neural networks6,7 and genetic algorithms8,9 are being used to build statistical noise models. Spatial interpolation is regarded as a common approach in the cluster of statistical methods. However, there is no standardized spatial interpolation method that can deal with the logarithmically reduction of noise levels with distance.10 Statistical models are often criticized for problems of limited reliability and applicability, since they cannot systematically relate physical surroundings to urban noise. A common criticism of existing models is that the results are not presented in an easily visualized format. Noise mapping is then developed in order to present noise levels over a particular geographic area. Noise maps usually entail a layer that is superimposed on noise and geographical information. While many cities have developed a 2D noise map, a few, including Paris and Hong Kong, have even updated to a 3D model.11,12 Noise mapping is the main method of modeling regional noise. These maps can be framed based on real-time monitoring or a combination of on-site monitoring and background simulation.13 By doing so, a static or a dynamic noise map can be visualized. However, the method of noise mapping is often time-consuming and expensive Received: March 8, 2011 Accepted: July 19, 2011 Revised: June 30, 2011 Published: July 19, 2011 7358
dx.doi.org/10.1021/es200785x | Environ. Sci. Technol. 2011, 45, 7358–7364
Environmental Science & Technology
ARTICLE
Figure 1. Current land use of Dalian Municipality and distribution of monitoring sites. Data sources: current land use data are interpreted based on the Dalian Municipality Master Plan (20092020), provided by the China Academy of Urban Planning and Design. Notes: other land use types include public facilities, warehouse land, municipal utilities, specially designated land, water area, and unutilized land.
in data collection. Furthermore, since these methods rely on both experiment-based models and/or the statistical models, they have both the advantage and disadvantage of these two kinds of models. It is obvious that current approaches have a difficulty in forecasting urban noise because there is almost no explicit information on future noise sources provided by an urban planning, which usually defines overall land use layout instead of sufficient details in guiding development and construction.1416 Therefore, there is an urgent need to develop a systematical urban noise model that links to land use change with limited and uncertain data at a planning stage to serve smart decision-making for urban development governance and environmental management. Land use regression (LUR) method is an important approach as it predicts long-term average pollution concentrations at a given site based on surrounding land use and traffic characteristics.17 To date, LUR has been used extensively for air pollution exposure analysis and environmental health research in Europe and North America.1823 Meanwhile, LUR started to see its applications in developing countries, such as in China.24 The main strength of LUR is the empirical structure of the regression mapping and its relatively simple inputs and low cost.17 For air pollution modeling, LUR has been shown to outperform geostatistical methods such as Kriging.25,26 In the comparisons between LUR methods and dispersion modeling, LUR models can perform mostly as well as dispersion models in explaining spatial variation.25,27 There is no standardized methodology for LUR yet, but descriptions of the general modeling process were discussed in many studies.25,2830 LUR models usually use uniformly shaped buffers (i.e., circular buffers or grids), but there are also attempts to create catchment-based buffer shapes in modeling wood smoke using a LUR model.31 Most studies use standard linear regression techniques to develop LUR models. In selection of a parsimonious model, current studies usually use the best subsets, manual forward,32 automated stepwise selection process,33 or distance decay regression selection strategy.34 Most studies are not confined with existing routine monitoring data but have undertaken supplementary monitoring in order to take all kinds of land use into account. The amount of supplementary monitoring
sites vary considerably among different studies but normally range from 20 to 100.35 This paper, for the first time, introduces a land use regression (LUR) approach, which has been recently introduced to modeling urban air pollution to develop a regional environmental noise model based on a GIS platform, to systematically examine the association between urban land use and environmental noise change. After a brief introduction of the LUR method, a land use based noise model is developed for Dalian Municipality, Northwest China. The results are then discussed in the final section.
2. MATERIALS AND METHODS This study followed the normal processes to construct a LUR model for depicting urban environmental noise (abbr. LUNOS, hereinafter). The day-time equivalent sound level (Ld, equivalent continuous A-weighted sound pressure level (Leq) measured during 6 a.m. to 10 p.m., according to the regulatory standard (GB 3096-2008)) at a specific location (s) was used as a dependent variable, and the areas of various land use (x) within ring-like buffers around the location were defined as independent variables. Study Area and Data Collection. Dalian Municipality, located in the southern part of the Liaodong Peninsula in northeast China, spans 13,538 km2 and consists of 7 districts and 4 countylevel cities (cf. Figure 1). The study area is confined within urban areas defined by the Dalian Master Plan (20092020). It consists of six urban districts accounting for 2415.0 km2 in total, a home of population 2 million. There were 172 routine monitoring sites (rms) supervised by the local environmental protection bureau in the study area. Monitoring data during May 427, 2008 were applied in this study. The RMSs were concentrated mainly in the city center and covered only a few kinds of land use (cf. Figure 1), which were not sufficient to construct the LUR model according to pretest modeling. Therefore, 30 purpose-designed monitoring sites (PMS) were identified in the suburbs (cf. Figure 1). The specific locations of the PMS were determined with the aids of digital city map and land use map. During field work, a GPS assistant was 7359
dx.doi.org/10.1021/es200785x |Environ. Sci. Technol. 2011, 45, 7358–7364
Environmental Science & Technology
ARTICLE
Table 1. Description of Independent Variablesd category (N variables)
description area (in km2) of 6 land use typesa
land use
subcategories IND1, IND2, IND3
buffer radii (m)c 20, 60, 100, 140
methods calculate the area of each land use type within each of the radiusb
(three types of industrial land), COM (commercial land) RES (residential land), GRN (green space) road length
length (in km) of 3 road types
RD1 (urban road),
calculate the length of each road type within each of the radiusb
RD2 (highway), RD3 (railway) a
The land use types were divided according to the Standard for Classification of Urban Land (GB137-90) stipulated by the Ministry of Construction. Industrial land was divided into three types where Ind1 stands for industrial land that has almost no disturbance and pollution to the environment, and ind3 represents industrial land that has serious environmental pollution. b The buffers were generated using the buffer functions at the ArcGIS 9.2 platform. c Calculations were performed using a vector data structure at the ArcGIS 9.2 platform. d The spatial variables were named with the subcategories and the buffer radius. For example, IND1_100 means the total area (in km2) of the first type of industrial land within the 100 m-radius buffer ring.
provided. The equivalent sound level of the PMS was monitored during February 2324, 2009. All the measurements were carried out with the NL-04 sound level meter following the steps stipulated by the National Environmental Quality Standard for Noise (GB 3096-2008). It is reasonable to assume that these two groups of data are a consistent data series, because the physical mechanism of noise transmission does not rely on temporal change and there was no significant land use change in the study area during this period. Further, these 202 monitoring sites were randomly assigned to two subsets: 101 were used for constructing the regression functions and the other half were used for model validation. Modeling and Validation. The noise sources of industry, transportation, and social activities, responsible for over 90% of urban noise pollution according to Dalian environmental statistics bulletin (2008), were taken into consideration. The study area was divided into 60,793 grids, each of which was 200 m 200 m. Ring-like buffers were designated based on noise transmission principles to generate dependent land use variables in LUR models. Considering the principle of noise attenuation and accuracy of spatial resolution, this study defined the radius of the circular buffers as 20 m, 60 m, 100 m, and 140 m; some are smaller than existing LUR models for air pollution. The areas of three kinds of industrial land, commercial land, residential land, and green space were accounted to indicate industrial and social sources, and the length of three different traffic lines were measured to represent the impacts of transportation land use.32,36 In total, 36 variables, divided into 2 categories and 9 subcategories, were generated around each sampling site, as described in Table 1. This study developed a linear model as many existing LUR studies did. The linear equation is described as below ð1Þ y ¼ R1 x1 þ R2 x2 þ 3 3 3 þ Rn xn þ c where y (dB) is the dependent variable represents environmental noise; xi is land use type; Ri and c are model parameters which are to be calibrated; and n represents the total number of variables. Meanwhile, a nonlinear model was developed to verify model structure by comparing the performance with that of the linear one. For the nonlinear model, the first step in the modeling process was to logarithmically transform the independent and dependent variables 0
ð2Þ y0 ¼ ln y, xi ¼ ln xi ði ¼ 1, 2 3 3 3 nÞ By doing so, the nonlinear function was transformed into a linear one, and thus it can be standardized in the uniform format as eq 1.
Table 2. Validation of the Linear LUNOS Model variable constant
value
std. error
t
prob > t
52.983
0.000
VIF
0.734
72.144
IND2_140
0.0002
0.000
1.758
0.082
RD2_140
0.0496
0.014
3.626
0.000
1.025
GRN_60 RD1_100
0.0003 0.0077
0.000 0.004
2.004 2.010
0.048 0.047
1.063 1.031
1.034
Model calibration was conducted via a process of multiple linear regressions. This paper processed an automated stepwise selection process (backward selection) using the software of SPSS13.0, with a significance level at 0.1 and a confidence level at 90%. In the backward selection process, the weakest variables are rejected. The process automatically ceases when all the remaining variables reached the significant level. To avoid high collinearity, the value of variance inflation factors (VIF) was limited to be smaller than 5. This paper compared the accuracy of the linear and nonlinear structures of the LUNOS model by using the monitoring data at the verification sites. The criterion was defined as the relative error of 10% at each site. The higher percentage of acceptable verification sites (PAS) reached the better the model performed. Meanwhile, the average and the root mean squared error (RMSE) of the relative errors were calculated. The LUNOS model was further applied to three different spatial scales, from a community level to a larger regional level, to test the model’s spatial reliability. The results were compared with a conventional spatial interpolation (SPI) model to examine the applicability of the LUR method and this geostatistical method.
3. RESULTS AND DISCUSSION The results suggested that there is no significant difference between the linear structure and nonlinear structure of LUNOS. Comparatively, the linear structure of LUNOS performed a bit better than the nonlinear one, as the PAS of the former one reached 83.2% and that of the latter one was 78.2%. For the relative errors at all the verification sites, the average and the RMSE of the linear structure (5.8%, 0.0026) and the nonlinear structure (6.2%, 0.0031) proved that the simulation results of the former one were more relatively stable. Therefore, the linear structure will be discussed in the following paragraphs. 7360
dx.doi.org/10.1021/es200785x |Environ. Sci. Technol. 2011, 45, 7358–7364
Environmental Science & Technology
ARTICLE
The equation of the linear LUNOS model is shown as below y ¼ 0:0002 IND2140 þ 0:0496 RD2140 -0:0003 GRN60 þ 0:0077 RD1100 þ 52:983 Table 2 summarizes the performance of the linear LUNOS model. Each individual variable has a significant score and acceptable multicollinearity. In order to examine the model reliability at different spatial scales, the linear LUNOS model was applied to three scales in case study area, i.e., urban sprawl area, urban central, and urban Table 3. Performance of the Linear LUNOS Model for Different Spatial Scales average of model
spatial scale
linear LUNOS downtown model
PAS
RMSE of
relative errors relative errors
75.0%
6.3%
78.4%
6.7%
0.0031
urban sprawl area 83.2%
5.8%
0.0026
urban central
0.0041
downtown (cf. Figure 1). The origin independent variables and the buffers used at the three domains were all the same. For each study area, the monitoring sites were randomly assigned to regression and verification subsets and the same modeling process as discussed previously were followed. Three spatial scales had variance being explained more than 70%. This result indicated that the application of this method to different spatial scales did not cause significant variation of accuracy, and this proved that the method was capable to model environmental noise at both the city level and the regional level. The LUNOS model at these three spatial scales selected the main noise sources in that region by the multiple linear regressions (cf. Table S1 and Table S2 in Supporting Information). The downtown-level results seem less accurate comparatively due to data limitation. All the monitoring data in downtown area were collected from the local routine monitoring network, and some land use information on independent variables, such as highway and industrial land Class II and Class III, were missing.
Figure 2. Urban environmental noise maps of the LUNOS model (a) and the SPI model (b) in 2008. 7361
dx.doi.org/10.1021/es200785x |Environ. Sci. Technol. 2011, 45, 7358–7364
Environmental Science & Technology
ARTICLE
Figure 3. Mapping urban environmental noise of Dalian Municipality in 2020 (a) and the exceedance to the GB standard (b) Data sources: land use data in 2020 are interpreted based on the Dalian Municipality Master Plan (20092020), provided by the China Academy of Urban Planning and Design.
The LUNOS model and a conventional spatial interpolation (SPI) model were applied to mapping urban environmental noise in 2008, as shown in Figure 2. The SPI model created the overall trends using the method of Kriging with the data of the same 101 regression sites. Considering the physical principle of noise propagation and attenuation, an exponential model was chosen with attenuation range limit of 1000 m. The equivalent sound pressure levels at these verification sites simulated by the LUNOS model and SPI model, respectively, were compared with actual monitoring data. It is apparent that the SPI model was much weaker in mapping urban environmental noise. The noise gradients became rather smooth at the areas where few or no monitoring data were available or even abruptly changed somewhere. In contrast, the LUNOS model can provide a more sophisticated noise map that contains much more details even at the areas where no monitoring data are available (cf. Figure 2). Moreover, the performance of the LUNOS model remained quite stable at different spatial scales while that of the SPI model significantly varied because the SPI model heavily depends on monitoring data. Therefore, we believed that the LUNOS generated noise maps are a reasonable reflection of actual conditions as it systematically links environmental noise to local land use. It is important to remember that in forecasting urban environmental noise change, which is often required at the urban planning stage, using the SPI model presents many challenges. In contrast, the LUNOS model can be easily applied for noise forecasting at a given land use layout, as shown in Figure 3(a). These results suggests that the average sound pressure levels at downtown, urban central, and urban sprawl area in 2020 could account for 53.2, 52.6, and 51.9 dB, respectively. Furthermore, areas where sound pressure levels would exceed the regulatory standard (GB 3096-2008) (cf. Table S3 in Supporting Information) can be identified, as shown in Figure 3(b). While the downtown areas would continue to be exposed to urban noise, some newly developing areas proposed in the urban master plan may become high priority with regard to noise pollution control. Transportation lands, among others, were to be dominant neighborhood environmental noise. This study proposed a robust method for modeling environmental noise at the regional level. Although the LUR technique has been applied for urban air exposure study, this study for the first time introduces the LUR model to urban noise modeling.
The LUNOS model has demonstrated its value to mapping urban noise in relation with urban land use change as well as the feasibility for application at different spatial scales. When faced with various uncertainties of urban development, the LUR method is a superior method to other conventional urban noise modeling approaches. By developing the association between environmental noise and land use, the LUNOS model can be easily applied to forecast urban noise change and to identify the potentially overstandard areas that would be in violation with regulatory requirements. This is of particular importance for urban planning and environmental management toward a smart decision-making at a planning stage. The LUR method provides a modeling procedure, rather than a fixed or inflexible model structure, for connecting environmental indicators with surrounding land use. Therefore, a LUR model is specific case oriented and could largely differ from others. Special attention should be paid to the screening of independent variables. When applying the LUR method to rapidly developing and expanding regions, some influential factors, e.g., a heavy manufacturing plant in urban area, may deserve exclusive consideration. Similar to Paris and Hong Kong, some megacities in China have been experiencing densely growth where a 3D appreciation of noise pollution is pertinent. The results showed the LUR method’s reliability in linking equivalent noise level and surrounding land use in a 2D format. As the noise propagation mechanism keeps consistent along different spatial directions, it is reasonable to believe that the LUR method could be easily extended to a 3D version via inventing a few new independent variables such as heights and topography. There is also a large space to improve the LUR method. First, the method defines environmental change only as a change in surrounding land use. This assumption makes a LUR model simple but also leads to difficulty when integrating with theoretical models. It is worth adding some functions based on the physical rules of noise propagation as well as some important factors to the LUNOS model, including key point sources, altitude, land cover, and others which may have significant influences, although it still remains challenging in traditional air exposure studies. Second, the more monitoring data are available, the better the model may perform. The specific location of sampling sites, however, rather than the actual amount of monitoring data may be more influential on model performance, as suggested by Ryan et al.37 Location-allocation algorithms for designing the purposedesigned monitoring sites are desired. Nevertheless, the modeling 7362
dx.doi.org/10.1021/es200785x |Environ. Sci. Technol. 2011, 45, 7358–7364
Environmental Science & Technology process can provide promising information to improve local monitoring networks and management, which often focuses on downtown areas and largely ignores regional environmental noise change that is significantly affected by urban expansion. Third, the shape and size of the buffers require more attention. While buffer sizes should be theoretically selected in accordance with known dispersion patterns,35 different buffer sizes could also lead to a large variation of simulation. In this study, 20 m was defined as the minimum radius of the circular buffer. A smaller size may be worthy of examination. Last but not least, the current LUR method is only capable of forecasting the long-term overall trend of the exposure to noise pollution as well as air pollution in elsewhere studies. The temporal dimension of the LUR model is often based on one year. A few recent studies31,38,39have introduced some new variables on a smaller temporal scale, i.e., one season, one day, or even one hour. It is also worth developing LUR-based urban noise models at a shorter temporal scale in future studies.
’ ASSOCIATED CONTENT
bS
Supporting Information. Table S1, the linear LUNOS model at urban central; Table S2, the linear LUNOS model at downtown, and Table S3, environmental quality standard for noise (GB 3096-2008). This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected].
’ ACKNOWLEDGMENT The authors appreciate Dr. Su and Dr. Brauer at UC Berkley for their suggestions on LUR modeling. The authors also thank Dalian Environmental Monitoring Center and Dalian Municipal Design and Research Institute of Environmental Science for the support on monitoring and data collection. The authors would like to thank the three anonymous reviewers for their valuable comments. ’ REFERENCES (1) Hothersall, D. C.; Chandler-Wilde, S. N. Prediction of the attenuation of road traffic noise with distance. J. Sound. Vib. 1987, 115 (3), 459–472. (2) Steele, C. A critical review of some traffic noise prediction models. Appl. Acoust. 2001, 62 (3), 271–287. (3) Tansatcha, M.; Pamanikabud, P.; Brown, A.; Affum, J. Motorway noise modelling based on perpendicular propagation analysis of traffic noise. Appl. Acoust. 2005, 66 (10), 1135–1150. (4) Lui, W.; Li, K.; Ng, P.; Frommer, G. A comparative study of different numerical models for predicting train noise in high-rise cities. Appl. Acoust. 2006, 67 (5), 432–449. (5) Pamanikabud, P.; Tansatcha, M.; Brown, A. Development of a highway noise prediction model using an L(eq)20s measure of basic vehicular noise. J. Sound. Vib. 2008, 316 (15), 317–330. (6) Weber, K.; Schlagner, W.; Schweier, K. Estimating regional noise on neural network predictions. Pattern Recognit. 2003, 36 (10), 2333–2337. (7) Givargis, S.; Karimi, H. A basic neural traffic noise prediction model for Tehran’s roads. J. Environ. Manage. 2010, 91 (12), 2529–2534.
ARTICLE
(8) Gundogdu, O.; Gokdag, M.; Yuksel, F. A traffic noise prediction method based on vehicle composition using genetic algorithms. Appl. Acoust. 2005, 66 (7), 799–809. (9) Rahmani, S.; Mousavi, S.; Kamali, M. Modeling of road-traffic noise with the use of genetic algorithm. Appl. Soft Comput. 2011, 11 (1), 1008–1013. (10) Stoter, J.; De Kluijver, H.; Kurakula, V. 3D noise mapping in urban areas. Int. J. Geogr. Inf. Sci. 2008, 22 (8), 907–924. (11) Butler, D. Noise management: Sound and vision. Nature 2004, 427 (6974), 480–482. (12) Wing, K.; Kwong, Visualisation of Complex Noise Environment by Virtual Reality Technologies. Environment Protection Department (EPD), Hong Kong. http://www.science.gov.hk/paper/EPD_ CWLaw.pdf (accessed 300107 2006). (13) Coelho, J. L. B.; Alarcao, D., Noise mapping and noise action plans in large urban areas. Forum Acusticum 2005. (14) Liu, Y.; Chen, J. N.; He, W. Q.; Zeng, S. Y.; Wu, H. B. Integrating uncertainty analysis into strategic environment al assessment of urban master plan: A methodology and case study. China Environ. Sci. 2007, 27 (4), 566–571. (15) Liu, Y.; Chen, J. N.; He, W. Q. A method of strategic environmental assessment of urban master plans. Acta Sci. Circumst. 2008, 28 (6), 1249–1255. (16) Liu, Y.; Chen, J. N.; He, W. Q.; Tong, Q. Y.; Li, W. F. Application of an Uncertainty Analysis Approach to Strategic Environmental Assessment for Urban Planning. Environ. Sci. Technol. 2010, 44 (8), 3136–3141. (17) Jerrett, M.; Arain, A.; Kanaroglou, P.; Beckerman, B.; Potoglou, D.; Sahsuvaroglu, T.; Morrison, J.; Giovis, C. A review and evaluation of intraurban air pollution exposure models. J. Exposure Anal. Environ. Epidemiol. 2005, 15, 185–204. (18) Nethery, E.; Leckie, S.; Marshall, J.; Brauer, M. Evaluation of a land-use regression model to assess exposure to air pollution during pregnancy: Use of GPS tracking and personal monitoring. Epidemiology 2006, 17 (6), S110–S110. (19) Su, J.; Larson, T.; Baribeau, A.; Brauer, M.; Rensing, M.; Buzzelli, M. Spatial modeling for air pollution monitoring network design: Example of residential woodsmoke. J. Air Waste Manage. Assoc. 2007, 57 (8), 893–900. (20) Jerrett, M.; Gale, S.; Kontgis, C. Spatial Modeling in Environmental and Public Health Research. Int. J. Environ. Res. Public Health 2010, 7 (4), 1302–1329. (21) Su, J.; Jerrett, M.; Beckerman, B.; Verma, D.; Arain, M.; Kanaroglou, P.; Stieb, D.; Finkelstein, M.; Brook, J. A land use regression model for predicting ambient volatile organic compound concentrations in Toronto, Canada. Atmos. Environ. 2010, 44 (29), 3529–3537. (22) Hoek, G.; Kos, G.; Beelen, R.; Dijkema, M.; de Hartog, J.; Brunekreef, B.; van der Zee, S. A Land Use Regression Model for Ultrafine Particles in Amsterdam. Epidemiology 2009, 20 (6), S76–S76. (23) Larson, T.; Henderson, S. B.; Brauer, M. Mobile Monitoring of Particle Light Absorption Coefficient in an Urban Area as a Basis for Land Use Regression. Environ. Sci. Technol. 2009, 43 (13), 4672–4678. (24) Chen, L.; Bai, Z. P.; Kong, S. F.; Han, B.; You, Y.; Ding, X. A.; Du, S. Y.; Liu, A. X. A land use regression for predicting NO2 and PM10 concentrations in different seasons in Tianjin region, China. J. Environ. Sci. (Beijing, China) 2010, 22 (9), 1364–1373. (25) Briggs, D.; de Hoogh, C.; Guiliver, J.; Wills, J.; Elliott, P.; Kingham, S.; Smallbone, K. A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments. Sci. Total Environ. 2000, 253 (13), 151–167. (26) Ross, Z.; Jerrett, M.; Ito, K.; Tempalski, B.; Thurston, G. A land use regression for predicting fine particulate matter concentrations in the New York City region. Atmos. Environ. 2007, 41 (11), 2255–2269. (27) Beelen, R.; Voogt, M.; Duyzer, J.; Zandveld, P.; Hoek, G. Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area. Atmos. Environ. 2010, 44 (36), 4614–4621. 7363
dx.doi.org/10.1021/es200785x |Environ. Sci. Technol. 2011, 45, 7358–7364
Environmental Science & Technology
ARTICLE
(28) Jerrett, M.; Arain, M.; Kanaroglou, P.; Beckerman, B.; Crouse, D.; Gilbert, N.; Brook, J.; Finkelstein, N.; Finkelstein, M. Modeling the intraurban variability of ambient traffic pollution in Toronto, Canada. J. Toxicol. Environ. Health, Part A 2007, 70 (34), 200–212. (29) Su, J.; Jerrett, M.; Beckerman, B. Modeling Intra-Urban Spatial Variability of Volatile Organic Compounds Using a Land Use Regression Method. Epidemiology 2008, 19 (6), S312–S313. (30) Vienneau, D.; de Hoogh, K.; Beelen, R.; Fischer, P.; Hoek, G.; Briggs, D. Comparison of land-use regression models between Great Britain and the Netherlands. Atmos. Environ. 2010, 44 (5), 688–696. (31) Larson, T.; Su, J.; Baribeau, A.; Buzzelli, M.; Setton, E.; Brauer, M. A spatial model of urban winter woodsmoke concentrations. Environ. Sci. Technol. 2007, 41 (7), 2429–2436. (32) Henderson, S.; Beckerman, B.; Jerrett, M.; Brauer, M. Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ. Sci. Technol. 2007, 41 (7), 2422–2428. (33) Aguilera, I.; Sunyer, J.; Fernandez-Patier, R.; Hoek, G.; -Alfaro, A.; Meliefste, K.; Bomboi-Mingarro, M.; Nieuwenhuijsen, M.; HerceGarraleta, D.; Brunekreef, B. Estimation of outdoor NOx, NO2, and BTEX exposure in a cohort of pregnant women using land use regression modeling. Environ. Sci. Technol. 2008, 42 (3), 815–821. (34) Su, J.; Jerrett, M.; Beckerman, B. A distance-decay variable selection strategy for land use regression modeling of ambient air pollution exposures. Sci. Total Environ. 2009, 407 (12), 3890–3898. (35) Hoek, G.; Beelen, R.; de Hoogh, K.; Vienneau, D.; Gulliver, J.; Fischer, P.; Briggs, D. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos. Environ. 2008, 42 (33), 7561–7578. (36) Briggs, D.; Collins, S.; Elliot, P.; Fischer, P.; Kingham, S.; Lebret, E.; Pryl, K.; Hv, R.; Smallborne, K.; Avd, V. Mapping urban air pollution using GIS: a regression-based approach. Int. J. Geogr. Inf. Sci. 1997, 11, 699–718. (37) Ryan, P. H.; LeMasters, G. K. A review of land-use regression models for characterizing intraurban air pollution exposure. Inhalation Toxicol. 2007, 19 (Suppl 1), 127–133. (38) Su, J.; Brauer, M.; Ainslie, B.; Steyn, D.; Larson, T.; Buzzelli, M. An innovative land use regression model incorporating meteorology for exposure analysis. Sci. Total Environ. 2008, 390 (23), 520–529. (39) Nectarios, R.; Christine, C.; Robert, G.; Guy, B. M. Validation of a Spatiotemporal Land Use Regression Model Incorporating Fixed Site Monitors. Environ. Sci. Technol. 2010, 45 (1), 294–299.
7364
dx.doi.org/10.1021/es200785x |Environ. Sci. Technol. 2011, 45, 7358–7364
