PM 10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM 10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM 10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM 10 prediction. A review of the spatial predictions of PM 10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM 10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM 10 , only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ď 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non-linear modeling procedure.

Surface urban heat island (SUHI) is defined as the elevated land surface temperature (LST) in urban area in comparison with non-urban areas, and it can influence the energy consumption, comfort and health of urban residents. In this study, the existence of daytime SUHI, in Cairo and its new towns during the summer, is investigated using three different approaches; (1) utilization of pre-urbanization observations as LST references; (2) utilization of rural observations as LST references (urban-rural difference); and (3) utilization of the SIUHI (Surface Intra Urban Heat Island) approach. A time series of Landsat TM & ETM+ data (46 images) from 1984 to 2015 was employed in this study for daytime LST calculation during summer. Different statistical hypothesis tests were utilized for the evaluation of LST and SUHI in the case studies. The results demonstrated that there is no significant LST difference between the urban areas studied, and their corresponding built-up areas. In addition, daytime LST in new towns during the summer is 2 K warmer than in Cairo. Utilization of a pre-urbanization observations approach, alongside an evaluation of the long-term trend, demonstrated that there is no daytime SUHI during the summer in the study areas, and construction activities in the study areas do not result in cooling or warming effects. Utilization of the rural observations approach showed that LST is lower in Cairo than its surrounding areas. This demonstrates why the selection of suitable rural references in SUHI studies is an important and complicated task, and how this approach may lead to misinterpretation in desert city areas with significant landscape and surface difference with their most surrounding areas (e.g., Cairo). Results showed that, although SIUHI technique can be representative for the changes of variance of LST in urban areas, it is not able to identify the changes of mean LST in urban areas.

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a b s t r a c tParameter and structure identifications are necessary in any modelling which aims to achieve a generalised model. Although ANFIS (Adaptive Network-based Fuzzy Inference System) employs well-known parameteridentification techniques, it needs to structure identification techniques for the determination of an optimum number of fuzzy rules and the selection of significant input variables from among the candidate input variables. In this study, a new structure identification scheme is developed and introduced, which is simultaneously capable of the selection of significant input variables and the determination of an optimum number of rules. This new structure identification was joined to ANFIS, and this joined modelling framework was applied to the simulation of virtual air-pollution monitoring stations in Berlin. In this study, 18 virtual particulate matter stations were simulated using the particulate matter data of some of the current stations. In other words, the particulate matter monitoring network of Berlin has been intensified. The evaluation of simulated virtual stations shows that, although the uncertainty of daily particulate matter measurement is about 10 percent, the simulated virtual stations can estimate the mean daily particulate matter with less than 10 percent of error. Mean absolute error and root mean square error of the simulations are less than 2.4 and 3.4 mg/m 3 , respectively. The correlation coefficient of the simulation results was more than 0.94. In addition, the range of mean bias error is between À 1.0 and 0.5 mg/m 3 , and the range of factor of exceedance is between À 14.8 and 10.8 percent. It means that the simulated virtual stations have a small bias. These results demonstrated the appropriate performance of the joined new structure identification scheme and ANFIS for development of a virtual air pollution monitoring network.

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