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

Chaudhari, P. R.; Gajghate, D. G.; Dhadse, Sharda; Suple, Sonali; Satapathy, Deepty Ranjan; Wate, S. R.

doi:10.1007/s10661-007-9649-7

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…Guoke et al (2009) examined detectability of six shrub and tree species in 20 × 20-m plots, and found that detectability ranged from 0.09 to 0.24 on average. These findings contrast with the assumption of many conventional flora surveys, that the majority of persistent vegetation species are highly or perfectly detected (for example, Chaudhari et al 2007). We and others believe that this assumption is drawn from the simple but flawed logic that due to their sedentary nature, plants are easier to detect than animals (Slade et al 2003).…”

Section: Introductionmentioning

confidence: 60%

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

Clarke

Lewis

Brandle³

et al. 2011

Environ Monit Assess

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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.

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Section: Introductionmentioning

confidence: 60%

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

Clarke

Lewis

Brandle³

et al. 2011

Environ Monit Assess

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“…Idso et al (2002) also reported a marginal difference in CO 2 concentrations between the urban and sub urban sites during the morning time and high difference during the night time. In addition, several other studies reported the presence of higher CO 2 concentrations at urban sites than at the background or rural sites (George et al, 2007;Idso et al, 2001;Gratani and Varone, 2005;Henninger and Kuttler, 2010;Rice and Bostrom, 2011;García et al, 2012;Chaudhari et al, 2007;Büns and Kuttler, 2012;Briber et al, 2013). Table 2 shows the intensity of CO 2 domes in various cities across the world.…”

Section: Comparison Of Co 2 Concentrations In Different Land Usesmentioning

confidence: 99%

Characteristics of ground level CO2 concentrations over contrasting land uses in a tropical urban environment

Kumar

Nagendra

2015

Atmospheric Environment

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“…We saw a drop in the CO 2 mixing ratio during the rainfall period due to changes in various processes, such as enhanced vertical mixing, uptake of CO 2 by vegetation and soils, and, where relevant, reduction in combustion sources. CO 2 can also dissolve into rainfall, forming carbonic acid, which may lead to a small decrease in the CO 2 mixing ratio as has been observed during high-intensity rain- fall (Chaudhari et al, 2007;Mahesh et al, 2014). The monsoon is also the growing season with higher CO 2 assimilation by plants than in other seasons (Sreenivas et al, 2016).…”

Section: Monthly and Seasonal Variationsmentioning

confidence: 93%

Seasonal and diurnal variations in methane and carbon dioxide in the Kathmandu Valley in the foothills of the central Himalayas

Mahata

Panday

Rupakheti³

et al. 2017

Atmos. Chem. Phys.

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Abstract. The SusKat-ABC (Sustainable Atmosphere for the Kathmandu Valley-Atmospheric Brown Clouds) international air pollution measurement campaign was carried out from December 2012 to June 2013 in the Kathmandu Valley and surrounding regions in Nepal. The Kathmandu Valley is a bowl-shaped basin with a severe air pollution problem. This paper reports measurements of two major greenhouse gases (GHGs), methane (CH 4 ) and carbon dioxide (CO 2 ), along with the pollutant CO, that began during the campaign and were extended for 1 year at the SusKat-ABC supersite in Bode, a semi-urban location in the Kathmandu Valley. Simultaneous measurements were also made during 2015 in Bode and a nearby rural site (Chanban) ∼ 25 km (aerial distance) to the southwest of Bode on the other side of a tall ridge. The ambient mixing ratios of methane (CH 4 ), carbon dioxide (CO 2 ), water vapor, and carbon monoxide (CO) were measured with a cavity ring-down spectrometer (G2401; Picarro, USA) along with meteorological parameters for 1 year (March 2013-March 2014. These measurements are the first of their kind in the central Himalayan foothills. At Bode, the annual average mixing ratios of CO 2 and CH 4 were 419.3 (±6.0) ppm and 2.192 (±0.066) ppm, respectively. These values are higher than the levels observed at background sites such as Mauna Loa, USA (CO 2 : 396.8 ± 2.0 ppm, CH 4 : 1.831 ± 0.110 ppm) and Waliguan, China (CO 2 : 397.7 ± 3.6 ppm, CH 4 : 1.879 ± 0.009 ppm) during the same period and at other urban and semi-urban sites in the region, such as Ahmedabad and Shadnagar (India). They varied slightly across the seasons at Bode, with seasonal average CH 4 mixing ratios of 2.157 (±0.230) ppm in the pre-monsoon season, 2.199 (±0.241) ppm in the monsoon, 2.210 (±0.200) ppm in the post-monsoon, and 2.214 (±0.209) ppm in the winter season. The average CO 2 mixing ratios were 426.2 (±25.5) ppm in the pre-monsoon, 413.5 (±24.2) ppm in the monsoon, 417.3 (±23.1) ppm in the postmonsoon, and 421.9 (±20.3) ppm in the winter season. The maximum seasonal mean mixing ratio of CH 4 in winter was only 0.057 ppm or 2.6 % higher than the seasonal minimum during the pre-monsoon period, while CO 2 was 12.8 ppm or 3.1 % higher during the pre-monsoon period (seasonal maximum) than during the monsoon (seasonal minimum). On the other hand, the CO mixing ratio at Bode was 191 % higher during the winter than during the monsoon season. The enhancement in CO 2 mixing ratios during the pre-monsoon season is associated with additional CO 2 emissions from forest fires and agro-residue burning in northern South Asia in addition to local emissions in the Kathmandu Valley. Published CO/CO 2 ratios of different emission sources in Nepal and India were compared with the observed CO/CO 2 ratios in this study. This comparison suggested that the major sources in the Kathmandu Valley were residential cooking and vehicle exhaust in all seasons except winter. In winter, brick kiln emissions were a major source. Simultaneous measurements in Bode and Chanban (15 July-3 O...

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

Cited by 17 publications

References 12 publications

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

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

Characteristics of ground level CO2 concentrations over contrasting land uses in a tropical urban environment

Seasonal and diurnal variations in methane and carbon dioxide in the Kathmandu Valley in the foothills of the central Himalayas

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