Monitoring of environmental parameters for CO2 sequestration: a case study of Nagpur City, India

A fuzzy logic enhanced environmental protection education model for policies decision support in green community development

This study proposes the promotion of environmental protection education among communities as a solution to the serious problems of high energy consumption and carbon emissions around the world. Environmental protection education has direct and lasting influences on everyone in society; therefore, it is helpful in our fight against many serious problems caused by high energy consumption. In this study, the Delphi method and the fuzzy logic theory are used to develop a quantizing assessment model based on qualitative analysis. This model can be used to assess the results and influences of community residents' participation in environmental protection education on green community development. In addition, it can be used to provide references for governing authorities in their decision making of green community development policies.

Monitoring variation in greenhouse gases concentration in urban environment of Delhi

Cities across the globe are considered as major anthropogenic sources of greenhouse gases (GHG), yet very few efforts has been made to monitor ambient concentration of GHG in cities, especially in a developing country like India. Here, variations in the ambient concentrations of carbon dioxide (CO(2)) and methane (CH(4)) in residential, commercial, and industrial areas of Delhi are determined from fortnightly daytime observations from July, 2008 to March, 2009. Results indicate that the average daytime ambient concentration of CO(2) varied from 495 to 554 ppm in authorized residential areas, 503 to 621 ppm in the slums or jhuggies in the unauthorized residential areas, 489 to 582 ppm in commercial areas, and 512 to 568 ppm in industrial areas with an average of 541 ± 27 ppm. CH(4) concentration varied from 652 to 5,356 ppbv in authorized residential areas, 500 to 15,220 ppbv in the unauthorized residential areas, 921 to 11,000 ppbv in the commercial areas, and 250 to 2,550 ppbv in the industrial areas with an average of 3,226 ± 1,090 ppbv. A low mid-afternoon CO(2) concentration was observed at most of the sites, primarily due to strong biospheric photosynthesis coupled with strong vertical mixing.

Identification and evaluation of air-pollution-tolerant plants around lignite-based thermal power station for greenbelt development

INTRODUCTION

Thermal power plants emit various gaseous and particulate pollutants into the atmosphere. It is well known that trees help to reduce air pollution. Development of a greenbelt with suitable plant species around the source of emission will mitigate the air pollution. Selection of suitable plant species for a greenbelt is very important.

MATERIALS AND METHODS

Present study evaluates different plant species around Neyveli thermal power plant by calculating the Air Pollution Tolerance Index (APTI) which is based on their significant biochemical parameters. Also Anticipated Performance Index (API) was calculated for these plant species by combining APTI values with other socio-economic and biological parameters.

DISCUSSION

Based on these indices, the most appropriate plant species were identified for the development of a greenbelt around the thermal power plant to mitigate air pollution. Among the 30 different plant species evaluated, Mangifere indica L. was identified as keystone species which is coming under the excellent category.

CONCLUSION

Ambient air quality parameters were correlated with the biochemical characteristics of plant leaves and significant changes were observed in the plants biochemical characteristics due to the air pollution stress.

Non-detection errors in a survey of persistent, highly-detectable vegetation species

Rare, small or annual vegetation species are widely known to be imperfectly detected with single site surveys by most conventional vegetation survey methods. However, the detectability of common, persistent vegetation species is assumed to be high, but without supporting research. In this study, we evaluate the extent of false-negative errors of perennial vegetation species in a systematic vegetation survey in arid South Australia. Analysis was limited to the seven most easily detected persistent vegetation species and controlled for observer skill. By comparison of methodologies, we then predict the magnitude of non-detection error rates in a second survey. The analysis revealed that all but one highly detectable perennial vegetation species was imperfectly detected (detection probabilities ranged from 0.22 to 0.83). While focussed in the Australian rangelands, the implications of this study are far reaching. Inferences drawn from systematic vegetation surveys that fail to identify and account for non-detection errors should be considered potentially flawed. The identification of this problem in vegetation surveying is long overdue. By comparison, non-detection has been a widely acknowledged, and dealt with, problem in fauna surveying for decades. We recommend that, where necessary, vegetation survey methodology adopt the methods developed in fauna surveying to cope with non-detection errors.