The article considers the actual problem of the ecology of urban areas associated with air pollution by industrial and energy enterprises. The study is aimed at substantiating the method of forming a plan for atmospheric air pollution observation using mobile laboratories. The quantitative characteristic of sites for different urban development zones is determined as a set of estimates. The air pollution index was chosen as a total sanitary and hygienic criterion for assessing pollution. The implementation of the decision-making problem using the analytic network process method is considered. Parameters of the city and environment that affect the assessment of atmospheric air quality were selected in the course of the study. A network structure of mutual influence of the studied parameters is formed. The architecture of the decision support system based on the proposed method is given. The created software makes it possible to automate the modeling of the distribution of pollutants in the atmosphere. The computational experiment is presented on the example of the Volgograd agglomeration, Russia. The proposed approach can significantly improve the organization of monitoring by mobile laboratories and reduce the cost of obtaining data on urban air pollution without increasing the number of existing observation laboratories, both stationary and mobile.

https://www.mdpi.com/1996-1073/16/4/1766/pdf?version=1676015707

Atmospheric Ecology Modeling for the Sustainable Development of the Urban Environment

Horizontal resolution, one of dominant variables for grid based Digital Elevation Model (DEM), directly determines
topographic expression, the accuracy of terrain parameters and geosciences simulations based on DEM. Cell size is
determined on relationship between resolution and terrain parameters traditionally, without taking terrain variance
information content of the raw data into account. This paper puts forward two methods for suitable DEM horizontal
resolution by mining the input contour data based on geostatistics. One is a direct method considering internal and
external variance. Regularization variables of serial resolutions are calculated from the sampled data by regularization
theory in geostatistics. After the variance comparison between point and serial resolutions, the grid at which the external
variance between adjacent grids is larger than average internal variance in grid is named suitable resolution. The other
method, which combines macro-topographical variance with micro-topographical variance, is an indirect way. Various
large-scale supports and their regularization variables are made by dividing the sampled data using regularization theory.
In order to ascertain an optimal support size to express macroscopic spatial variance of terrain, semivariograms of
regularization variables are analyzed on various support sizes. Estimation of the optimal bin size that can estimate the
probability density function in non-parametric density estimation is referred to decide the microcosmic appropriate
resolution in the optimal support. Both methods were experimented in practice, and gave relatively consistent results.
The latter was commended considering the computational efficiency.

Determination of suitable cell size for grid based digital elevation model

The damage of dwelling houses constitutes the primary cause of casualties and asset loss in seismic disasters that occurred in Chinese rural areas. The structure of houses is crucial for assessing the vulnerability of rural houses. However, at present, available data on rural housing structure are incomplete and their spatial scales are inconsistent. This paper estimated the amount and ratio of rural houses in five structures, namely 'wood', 'brick', 'mixed', 'reinforced concrete', and 'other', for 2380 counties across China. With the percent- age sampling census data in 2005, four accuracy levels were specified. Then, a set of down-scaling models were established, where the impact of climate, economic development level and ethnic minority cultural factors on rural housing structure, as well as the spatial autocorrelation of neighboring spatial units were considered. Based on the estimation results, a database of county-level rural housing structure was established, based on which the vul- nerability of rural houses in different areas was clarified.

Estimation of rural housing structure and its vulnerability in China

In the course of studying on regional land use potential evaluation a certain spatial basic unit is often used as the analysis
groundwork. The analysis result relies on unit division methods and research scales. In the same area space unit
characters correlate with the space scales. In geography, the conclusions made on a scale can't be applied to other scales.
Modifiable Area Unit Problem, MAUP, is the classical theory to solve the effects of spatial scale, that is, there are many
different ways to divide the surface into non-overlapping million units for spatial analysis. This paper studies on the
spatial unit scale transformation process of land use potential evaluation in the same area on different scales, such as at
local, regional and global levels .etc. Characteristic Scales is defined in the transformation process from large scale to
Small Scale. Then the fractal characteristic of spatial unit is raised on different scales to form land use potential
geospatial hierarchy. Using geo-statistical procedures, we mainly study on the transformation progress of SUs of land
use potential evaluation on different scales in the same area, define the eigenvalue of SUs on each scale. We find the
definition of spatial units (SUs) makes a point of the spatial analyses results on regional scales. Finally, aiding the fractal
dimension, the fractal characteristic of spatial unit is raised in a broader area. Land use potential geospatial hierarchical
structure is explored to aid the regional development policymaking.

Spatial units of land use potential evaluation on regional scales

To discuss the spatial interpolation based on panel data with spatial autocorrelation, the first-order spatial autoregressive interpolation model and the Kriging algorithm interpolation model are established from the perspective of the cross-sectional data. Genetic algorithm back-propagation neural network interpolation model is established from the perspective of the time-series data. A spatial combination interpolation model is established by the results of these models. The weights of the combination model is calculated by a new method of spatial drift. An empirical study is carried out with interpolation some areas’ GDP per capita in Fujian 2007, China. The result shows that the most effective one is the spatial combination interpolation model.

Spatial Combination Interpolation Model Based on Panel Data and Its Empirical Study

GIS is a geo-referenced data based system for manipulating digital spatial and thematic data. Because of its unique properties of spatial data (spatial dependence and spatial heterogeneity), there is an urgent need to investigate the mechanisms of spatial processes through exploring spatial data. The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) reversing information from complicated spatial data. To meet the real world solutions, a procedural diagram of spatial data analysis is suggested as below: (1) preliminary data treatment; (2) modeling; (3) solution; and (4) interpretation. Various techniques are being employed to investigate spatial processes, the identical study objective and some common characteristics of the study object may lead to the integration and convergence of the various approaches, and further the introduction of a general science: spatial mathematics. A same important issue is to develop robust spatial analysis computer packages for public uses.

Techniques of spatial data analysis

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Techniques of spatial data analysis