A hybrid Grey-Markov/ LUR model for PM_(10) concentration prediction under future urban scenarios

摘要

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 mu g/m(3) vs. 21.31 mu g/m(3)). The economic interest scenario identifies the largest demands of future built-up (2 306.50 km(2)) and bare (34.88 km(2)) 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 km(2), respectively. Correspondingly, the economic interest scenario identifies the severest PM10 pollution with the highest mean predicted concentration of 53.78 mu g/m(3) and the largest percent (19.43%) of area exceeding the Level 2 value (70 mu g/m(3)) of Chinese National Ambient Air Quality Standard (CNAAQS); these are significantly higher than those of the business as usual scenario (49.63 mu g/m(3), 6.28%). The resource-conserving scenario (46.79 mu g/m(3)) and ecological interest scenario (46.76 mu g/m(3)) 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.