Sensitizing Land Uses to Control Motor Vehicular Air Pollution: a Case Study of Lahore in Connection with Paris

Several factors influence air pollution, but effect of land uses is pivotal and has direct consequences on growth and development of a city. In order to assess consequent effects of land uses on air pollution, it is important to transform a set of variables into significant components by using principal component analysis. These components help to develop an effective model between air pollution and land uses by application of regression analysis. Lahore, titled as “Paris of the East,” has some featured commonalities with Paris and was jointly selected for research to adopt common policies and measures to combat air pollution. A two-step cluster analysis has been applied to testify resemblance of Paris to towns of Lahore as a cluster. This paper is trying to develop a calibrated model which helps to estimate about air pollution at a significant level with only one independent variable. Furthermore, it helps to control air pollution through sensitization of land uses in Lahore.     

The impacts of organizational green culture and corporate social responsibility on employees’ responsible behaviour towards the society

Environmental Science and Pollution Research - Corporate social responsibility (CSR) initiatives and organizational green culture (OGC) play a significant role in developing organizations and...     

Climate change and major crop production: evidence from Pakistan

Environmental Science and Pollution Research - Climatic changes are posing serious threats to crop production and food insecurity across the globe. This study explores the dynamic relationship...     

Assessing the long- and short-run asymmetrical effects of climate change on rice production: empirical evidence from India

Environmental Science and Pollution Research - In recent years, environmental change has arisen as a ubiquitous problem and gained environmentalist’s attention across the globe due to its...     

Do risk perceptions and constraints influence the adoption of climate change practices among small livestock herders in Punjab,Pakistan?

Environmental Science and Pollution Research - Climate change is severely damaging the agricultural system of many food producing regions worldwide. Small/subsistent livestock herders are the most...     

Land cover mapping based on random forest classification of multitemporal spectral and thermal images

Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8’s spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82 %, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation.