The Human Impact

Middleton, Nick

doi:10.1002/9780470710777.ch22

Cited by 6 publications

(1 citation statement)

References 45 publications

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“…In one study in an opencast coalmine area in Bina of India, the most annual amount of dust fall was 96.2 ± 3.6 g/m 2 .mon (29). In a study by Wangquan et al in 2004 in Gansa province of China, the amount of dust fall was 18.23 -69.34 ton/km 2 .month (30). In another study by Midelton in 1997 in Sub-Saharan region, the amount of dust fall was 0.3 -16.7 ton/km 2 .month (31) which was more than the amounts obtained in Kerman.…”

Section: Discussionmentioning

confidence: 94%

Urban Dust Fall Concentration and Its Properties in Kerman City, Iran

Malakootian

Ghiasseddin²,

Akbari

et al. 2013

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Background: Dust events are common air pollution events in parts of the world in arid, semiarid, or desert areas. During the last years, air quality of Kerman city has been deteriorated due to urban and industrial development as well as population growth. Objective: The aim of this study was to evaluate the dust fall events in Kerman city. Material and Methods:The British Jar Test assembly method was applied. Sampling sites were located in seven spots of the city. Forty two dust fall samples were monthly collected during 6 months (May-November, 2009). All test methods have been adopted from the ASTM guideline and Air Sampling Method and Analysis text. Results: Dust fall concentrations were analyzed as a function of times, locations and meteorological parameters. The results showed that the maximum total dust fall (TDF) was 24.42 g/m2.month and the minimum 6.56 g/m2.month at Resallat square station and Shahab Avenue, respectively. The average of monthly concentration of TDF had its maximum in the fifth period (Sept-Oct) which was 17.44 g/m2.mon. The average of monthly concentration of TDF had its minimum in the fourth period (Aug-Sep) which was 11.9 g/ m2.mon. TDF average concentration in the 6 month period of taking sample was 14.41 g/m2.mon. Conclusion:To sum up, there was no significant association between variables such as temperature, humidity, wind speed and dust concentration, whereas a significant association was found between precipitation variable and dust concentration. One -Way ANOVA Test demonstrated meaningful difference between mean of the TDF values in different stations and sampling period.

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Section: Discussionmentioning

confidence: 94%

Urban Dust Fall Concentration and Its Properties in Kerman City, Iran

Malakootian

Ghiasseddin²,

Akbari

et al. 2013

Health Scope

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Combating Climate Change, Desertification and Sandstorms: A Collaborative Approach

Zhang

Rong

et al. 2019

Research Series on the Chinese Dream and China’s Development Path

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Using artificial neural networks to predict future dryland responses to human and climate disturbances

Buckland

Bailey

Thomas

2019

Sci Rep

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Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropogenic pressures, identifying the role of external disturbances in driving geomorphic response is vitally important. We developed a novel approach, using artificial neural networks (ANNs) applied to time series of historical reactivation-deposition events from the Nebraska Sandhills, to determine the relationship between historic periods of sand deposition in semi-arid grasslands and external climatic conditions, land use pressures and wildfire occurrence. We show that both vegetation growth and sediment re-deposition episodes can be accurately estimated. Sensitivity testing of individual factors shows that localised forcings (overgrazing and wildfire) have a statistically significant impact when the climate is held at present-day conditions. However, the dominant effect is climate-induced drought. Our approach has great potential for estimating future landscape sensitivity to climate and land use scenarios across a wide range of potentially fragile dryland environments.

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The Human Impact

Cited by 6 publications

References 45 publications

Urban Dust Fall Concentration and Its Properties in Kerman City, Iran

Urban Dust Fall Concentration and Its Properties in Kerman City, Iran

Combating Climate Change, Desertification and Sandstorms: A Collaborative Approach

Using artificial neural networks to predict future dryland responses to human and climate disturbances

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