PM₁₀ prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM₁₀ prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM₁₀ 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 PM₁₀ prediction. A review of the spatial predictions of PM₁₀ shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM₁₀ in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM₁₀, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ≤ 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.

In this paper, a fuzzy modeling technique called Modified Active Learning Method (MALM) was introduced and utilized for fuzzy simulation of indoor and inner surface temperatures in residential buildings using meteorological data and its capability for fuzzy simulation was compared with other studies. The case studies for simulations were two residential apartments in the Fakouri and Rezashahr neighborhoods of Mashhad, Iran. The hourly inner surface and indoor temperature data were accumulated during measurements taken in 2010 and 2011 in different rooms of the apartments under heating and natural ventilation conditions. Hourly meteorological data (dry bulb temperature, wind speed & direction and solar radiation) were measured by a meteorological station and utilized with zero to three hours lags as input variables for the simulation of inner surface and indoor temperatures. The results of simulations demonstrated the capability of MALM to be used for nonlinear fuzzy simulation of inner surface and indoor temperatures in residential apartments.