A hybrid Grey-Markov/ LUR model for PM10 concentration prediction under future urban scenarios

Abstract Exploring the spatial distribution of air pollutants under future urban planning scenarios is essential as urban sprawl increases in China. However, existing published prediction models usually forecast pollutant concentrations at the station level or estimate spatial distribution of pollutant in a historical perspective. This study has developed a hybrid Grey-Markov/land use regression (LUR) model (GMLUR) for PM10 concentration prediction under future urban scenarios by employing the forecast of Grey-Markov model as surrogate measurements to calibrate the spatial estimations of LUR model. Taking the agglomeration of Changsha-Zhuzhou-Xiangtan (CZT) in China as a case, the superiority of GMLUR was tested and spatial distribution of PM10 concentrations based on four potential land use scenarios for the year 2020 were predicted. Results show that GMLUR modelling outperforms LUR modelling with clear lower average relative percentage error (5.13% vs. 24.09%) and root-mean-square error (5.50 μg/m3 vs. 21.31 μg/m3). The economic interest scenario identifies the largest demands of future built-up (2 306.50 km2) and bare (34.88 km2) areas. Built-up area demands for the business as usual scenario, resource-conserving scenario, and ecological interest scenario are 362.67, 1 042.22, and 1 014.70 km2, respectively. Correspondingly, the economic interest scenario identifies the severest PM10 pollution with the highest mean predicted concentration of 53.78 μg/m3 and the largest percent (19.43%) of area exceeding the Level 2 value (70 μg/m3) of Chinese National Ambient Air Quality Standard (CNAAQS); these are significantly higher than those of the business as usual scenario (49.63 μg/m3, 6.28%). The resource-conserving scenario (46.79 μg/m3) and ecological interest scenario (46.76 μg/m3) are cleaner with no area exceeding the Level 2 value of CNAAQS. It can be concluded that GMLUR modelling provides a feasible way to evaluate the potential outcome of future urban planning strategies in the perspective of air pollution.

[1]  Hichem Omrani,et al.  Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale , 2014 .

[2]  Xiaoping Liu,et al.  A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects , 2017 .

[3]  Yong Liu,et al.  A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations. , 2015, The Science of the total environment.

[4]  Anu W. Turunen,et al.  Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project , 2014, The Lancet.

[5]  Yunhui Liu,et al.  Land use pattern optimization based on CLUE-S and SWAT models for agricultural non-point source pollution control , 2013, Math. Comput. Model..

[6]  Bin Zou,et al.  Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective , 2015, Scientific Reports.

[7]  Paul Schot,et al.  Land use change modelling: current practice and research priorities , 2004 .

[8]  Noam Levin,et al.  High spatial resolution night-time light images for demographic and socio-economic studies , 2012 .

[9]  Xiaoping Liu,et al.  Satellite-based ground PM 2.5 estimation using timely structure adaptive modeling , 2016 .

[10]  Ki-Hyun Kim,et al.  A review on the human health impact of airborne particulate matter. , 2015, Environment international.

[11]  Beibei Sun,et al.  Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models , 2014 .

[12]  Michael Brauer,et al.  Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. , 2007, Environmental science & technology.

[13]  Predrag Hercog,et al.  Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations , 2009 .

[14]  M. Minguillón,et al.  2001-2012 trends on air quality in Spain. , 2014, The Science of the total environment.

[15]  PETER H. VERBURG,et al.  Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model , 2002, Environmental management.

[16]  Bert Brunekreef,et al.  Land use regression model for ultrafine particles in Amsterdam. , 2011, Environmental science & technology.

[17]  Min Huang,et al.  Predictive analysis on electric-power supply and demand in China , 2007 .

[18]  Mohammad Reza Lotfalipour,et al.  Prediction of CO2 Emissions in Iran using Grey and ARIMA Models , 2013 .

[19]  J. Hooyberghs,et al.  A neural network forecast for daily average PM10 concentrations in Belgium , 2005 .

[20]  Yaping Shao,et al.  Effects of soil moisture and dried raindroplet crust on saltation and dust emission , 2008 .

[21]  Bin Zou,et al.  High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression , 2016, IEEE Geoscience and Remote Sensing Letters.

[22]  Bin Zou,et al.  Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning , 2017, Remote. Sens..

[23]  B. Brunekreef,et al.  Spatial variations and development of land use regression models of oxidative potential in ten European study areas , 2017 .

[24]  Bin Zou,et al.  Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling , 2016, Remote. Sens..

[25]  Andrés Manuel García,et al.  Cellular automata models for the simulation of real-world urban processes: A review and analysis , 2010 .

[26]  Jianping Wu,et al.  Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data , 2014, Remote. Sens..

[27]  Ujjwal Kumar,et al.  Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India , 2010 .

[28]  John S. Gulliver,et al.  Development and transferability of a nitrogen dioxide land use regression model within the Veneto region of Italy , 2015 .

[29]  Sha Xu,et al.  Effect of Land Use and Cover Change on Air Quality in Urban Sprawl , 2016 .

[30]  Yi Li,et al.  Simulating the optimal land-use pattern in the farming-pastoral transitional zone of Northern China , 2008, Comput. Environ. Urban Syst..

[31]  A Simpson,et al.  Modelling air pollution for epidemiologic research--part II: predicting temporal variation through land use regression. , 2010, The Science of the total environment.

[32]  Ujjwal Kumar,et al.  ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO) , 2010 .

[33]  Paolo Vineis,et al.  Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: results from the ESCAPE and TRANSPHORM projects. , 2014, Environment international.

[34]  Wangshu Sun,et al.  Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China , 2016 .

[35]  Kazuhiko Ito,et al.  A land use regression for predicting fine particulate matter concentrations in the New York City region , 2007 .

[36]  Neng Wan,et al.  Land Use Regression Modeling of PM2.5 Concentrations at Optimized Spatial Scales , 2016 .

[37]  Bruce Misstear,et al.  Real time air quality forecasting using integrated parametric and non-parametric regression techniques , 2015 .

[38]  Coşkun Hamzaçebi,et al.  Forecasting the Energy-related CO2 Emissions of Turkey Using a Grey Prediction Model , 2015 .

[39]  Birgit Müller,et al.  Theoretical foundations of human decision-making in agent-based land use models - A review , 2017, Environ. Model. Softw..

[40]  Kurt Straif,et al.  The carcinogenicity of outdoor air pollution. , 2013, The Lancet Oncology.

[41]  Bin Zou,et al.  An optimized spatial proximity model for fine particulate matter air pollution exposure assessment in areas of sparse monitoring , 2016, Int. J. Geogr. Inf. Sci..

[42]  M. Brauer,et al.  High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. , 2017, Environmental science & technology.

[43]  Liang Zhai,et al.  An improved geographically weighted regression model for PM 2.5 concentration estimation in large areas , 2018 .

[44]  Yan Zhang,et al.  A land use regression model for estimating the NO2 concentration in Shanghai, China. , 2015, Environmental research.

[45]  Shuxiao Wang,et al.  Characteristics and health impacts of particulate matter pollution in China (2001–2011) , 2013 .

[46]  Peter Faber,et al.  Aerosol particle and trace gas emissions from earthworks, road construction, and asphalt paving in Germany: Emission factors and influence on local air quality , 2015 .

[47]  R. Gil Pontius,et al.  Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA , 2001 .