Determining potential planting areas in urban regions

Recent progress on phytoremediation of urban air pollution

The rapid growth of population and economy has led to an increase in urban air pollutants, greenhouse gases, energy shortages, environmental degradation, and species extinction, all of which affect ecosystems, biodiversity, and human health. Atmospheric pollution sources are divided into direct and indirect pollutants. Through analysis of the sources of pollutants, the self-functioning of different plants can be utilized to purify the air quality more effectively. Here, we explore the absorption of greenhouse gases and particulate matter in cities as well as the reduction of urban temperatures by plants based on international scientific literature on plant air pollution mitigation, according to the adsorption, dust retention, and transpiration functions of plants. At the same time, it can also reduce the occurrence of extreme weather. It is necessary to select suitable tree species for planting according to different plant functions and environmental needs. In the context of tight urban land use, the combination of vertical greening and urban architecture, through the rational use of plants, has comprehensively addressed urban air pollution. In the future, in urban construction, attention should be paid to the use of heavy plants and the protection and development of green spaces. Our review provides necessary references for future urban planning and research.

Geospatial measurement of urban sprawl using multi-temporal datasets from 1991 to 2021: case studies of four Indian medium-sized cities

In recent decades, medium-sized Indian cities have experienced accelerated urban growth due to the saturation of large cities. Such rapid urban growth combined with inadequate urban planning has triggered urban sprawl in medium-sized Indian cities. In this context, the present study focuses on the geospatial measurement of urban sprawl in four rapidly expanding Indian medium-sized cities located in diverse physiographic regions, such as Lucknow urban agglomeration (UA), Bhubaneswar UA, Raipur UA, and Dehradun UA. Multi-temporal Landsat imageries from 1991 to 2021 were downloaded for land cover classification through the maximum likelihood classification tool in ArcGIS 10.3. Thereafter, spatiotemporal land cover change detection was performed based on the classified land cover maps. The presence of urban sprawl was detected using the relative entropy index while the urban expansion index quantified the urban sprawl typologies such as edge expansion, leapfrog development, and ribbon development. The results exhibited a rapid rise in built-up land cover from 1991 to 2021. The prevalence of urban sprawl was detected in all four cities as per the relative entropy index. Edge expansion typology of urban sprawl was dominant compared to leapfrog development and ribbon development. Such urban growth phenomenon creates a hindrance in promoting sustainable urban development in medium-sized Indian cities. The results obtained from this paper would assist urban planners and policymakers in developing strategies to encourage planned urban growth. This paper exhibits the potential of geoinformatics to monitor and analyze urban sprawl.

Determining afforestation areas by using social, economic and ecological scales

Global anthropogenic damage is caused when humans aim to improve their welfare by social and economic activities. From this vantage, this paper seeks to determine priority locations for afforestation areas and carbon sinks by using socio-economic and ecological variables. Factor analysis is performed on degraded forest areas (DEGFRST), the ratio of non-forest areas to provincial general area (NFL), average of total monthly rainfall (ATMR), air pollution (PM10), the amount of migration (AMGR), annual average population density (AAPD), gross domestic product by industrial activity (I_GDP), socio-economic development index (SEDI) of provinces, export (EXP) and import (IMP) amount of provinces, average number of cars per one thousand people (ACNPT), and average electricity consumption per person (AECPP) variables for all provinces in Turkey (KMO = 0.802, Bartlett's χ² = 832.191, and p < 0.0001). Principal component analysis is used as a factor extraction method. Based on the three components obtained (explaining 74.730% of the total variance), the factor scores of 81 provinces were analyzed geostatistically using the Kriging interpolation method. The final map of potential afforestation areas was created using three-factor maps and factor variances, according to weighted overlay analysis. As a result of this study, afforestation priority areas in Turkey were identified based on three components. In subsequent studies, by increasing the number of variables used in this study, strategies for increasing Turkey's carbon sinks can be planned. Evaluating socio-economic and ecological factors together in afforestation studies can contribute to balancing human impact and conservation through alternative approaches.