Introduction
It is well known that anthropogenic activities play an important role
in drylands' climate change. Salinization, desertification, loss of
vegetative cover, loss of biodiversity, and other forms of environmental
deterioration are partly caused by anthropogenic activities (Huang et al.,
2016a, b). With rapid economic development, more fossil fuels have been
consumed, which produced a great deal of greenhouse gases (GHGs) as well as
energy (Barnett and O'Neill, 2010). The released GHGs and heat have induced a
strong influence on temperature spatial distribution in recent years (Li and
Zhao, 2012), especially in developing countries, where the economic policy relies on extensive growth, which favours results, despite
lower resource efficiency and energy waste.
Jiang and Hardee (2011) noted that main factors influencing anthropogenic
effects on aerosol emission are economic growth, technological change, and
population growth, which cannot be easily simulated using numerical models
(Zhou et al., 2010). Recently, better understanding about the effects of
human activities on dryland expansion in various scenarios has been achieved
(Huang et al., 2016b). It appears that higher densities of younger workers
are strongly correlated with increased energy use (Liddle, 2004), carbon
dioxide emission (Liddle and Lung, 2010; H. Huang et al., 2014), and energy
consumption, and the combined production of heat has been released into
the atmosphere along with GHGs. Although human activities play an important
role in the process of regional climate change, our understanding of their
relationship is extremely limited, especially in drylands (Jiang, 2010).
Huang et al. (2012) showed that drylands are most sensitive to global
warming; this warming was induced by dynamical and radiative factors. Guan et
al. (2015a) found that the enhanced warming in drylands was a result of
radiative-forced temperature, which has a close relationship with aerosol
column burden. The aerosol in drylands has an obvious warming effect (Huang
et al., 2006a, 2008; Chen et al., 2010; Ye et al., 2012; Jin et al., 2015),
and the aerosol has a wide distribution and tends to have a relatively
large optical depth (H. Huang et al., 2010; Bi et al., 2011; Liu et al.,
2011; Xu and Wang, 2015; Xu et al., 2015), leading to a significant radiative
effect in the drylands. According to the results of Tegen and Fung (1995), the
existing atmospheric dust load is hard to explain by natural sources alone.
The atmospheric dust load that originates from soil and is disturbed by human
activities, such as various land-use practices, can increase the overall dust
load and in turn affect radiative forcing. Efforts to quantify the relative
importance of different types of dust sources and the factors that affect
dust emissions are critical for understanding the global dust cycle, as well
as historical and possible future changes in dust emission (Okin et al.,
2011; Huang et al., 2015). Therefore, studies on different types of aerosols
are necessary in the study of radiative effect (J. Huang et al., 2009, 2014;
Wang et al., 2010; Yi et al., 2014).
Generally, the aerosols in drylands are divided into two categories: natural
and anthropogenic dusts. Anthropogenic dust originates predominantly from
agricultural practices (e.g. harvesting, ploughing, and overgrazing) and
changes in surface water (e.g. shrinking of the Caspian Sea, the Aral Sea,
and Owens Lake), as well as urban (e.g. construction) and industrial
practices (e.g. cement production and transport) (Prospero et al., 2002).
Over the past few decades, a combination of higher frequency of warmer and
dryer winters/springs in semi-arid and semi-wet regions and changes in
vegetated land cover due to human activities have likely increased
anthropogenic dust emission over different regions (Mahowald and Luo, 2003).
Mulitza et al. (2010) studied the development of agriculture in the Sahel,
which was associated with a large increase in dust emission and deposition in
the region, and they found that dust deposition is related to precipitation in
tropical West Africa on the century scale. Due to the importance of
anthropogenic dust in climate study, Huang et al. (2015) developed a
detection method of anthropogenic dust emission and presented a global
distribution of anthropogenic dust aerosol. The current consensus is that up
to half of the modern atmospheric dust load originated from anthropogenically
disturbed soils (Tegen et al., 2004). Such a great proportion of
anthropogenic dust will greatly influence local radiative forcing. Therefore,
influence of human activities on production of anthropogenic dust is critical
for predicting and estimating the radiative effect of aerosol in regional
climate change.
Most of previous results focused on the emission of natural dust aerosol
(Z. Huang et al., 2010; Li et al., 2011; Yi et al., 2011, 2012); the study on
anthropogenic dust is relatively limited. In this study, the anthropogenic
dust over semi-arid regions is identified by CALIPSO data, and its
relationship with human activities is investigated. The method used to
distinguish anthropogenic dust from the total dust aerosols is based on that
of Huang et al. (2015). This paper is organized as follows. Section 2
introduces the data sets used in this study. Section 3 presents the method
used to identify the anthropogenic dust aerosols in the semi-arid regions.
Section 4 discusses anthropogenic dust emission over global semi-arid regions
and its relationship to human activities, including a comparison among four
different semi-arid regions. Our major findings, followed by a discussion of
the radiative effect of anthropogenic dust on regional climate change in
semi-arid regions, are given in Sect. 5.
Data
The aridity index (AI) data set
In this study, we use the AI to classify different types of
regions. The AI is defined as the ratio of annual precipitation to annual
potential evapotranspiration, representing the degree of climatic dryness.
The AI data set used in this study (Feng and Fu, 2013; Huang et al., 2016b)
is
based on the Climate Prediction Center (CPC) data sets. Drylands are
identified as regions with AI values less than 0.65 and are further
classified into hyper-arid (AI < 0.05), arid (0.05 ≤ AI < 0.2), semi-arid (0.2 ≤ AI < 0.5), and dry sub-humid
(0.5 ≤ AI < 0.65) types following Middleton and Thomas (1997). Of the
four types, hyper-arid regions are the driest, followed by arid, semi-arid,
and dry sub-humid regions. The AI data set is provided by Feng and Fu (2013)
and cover the period from 1948 to 2008, with a spatial resolution of
0.5∘ by 0.5∘.
Population data
The population data are from the Gridded Population of the World data set,
version 3 (GPWv3,
http://sedac.ciesin.columbia.edu/data/collection/gpw-v3), which is
maintained by the Center for the International Earth Science Information
Network (CIESIN) and the Centro Internacional de Agricultura Tropical (CIAT).
GPWv3 depicts global population distribution. It is a gridded, or raster,
data product that renders global population data at the scale and extent
required to illustrate the spatial relationship between human population and
global environment. It aims to provide a spatially disaggregated population
compatible with data sets from social, economic, and earth science disciplines.
The spatial resolution is 0.5∘ × 0.5∘. The
population data estimates are for the years of 1990, 1995, 2000, 2005, and
2010.
Dust detection data
The instrument used to detect anthropogenic dust is the CALIPSO Cloud-Aerosol
Lidar with Orthogonal Polarization (CALIOP). CALIOP acquires vertical
profiles of elastic backscatter at two wavelengths (532 and 1064 nm) and
linear depolarization at 532 nm from a near-nadir viewing geometry for both
day and night (Hu et al., 2007a, b, 2009; Liu et al., 2008). The data sets
detail the information of Level-1 backscatter, depolarization ratio, and
colour ratio profiles along with the Level-2 Vertical Feature Mask (VFM)
product and the 5 km aerosol profile product. The CALIPSO algorithm uses
volume depolarization ratio (δv) greater than 0.075 to
identify dust (Omar et al., 2009). In the CALIPSO version 3 VFM data, the
cloud aerosol discrimination (CAD) algorithm can separate clouds and aerosols
based on multi-dimensional histograms of scattering properties (e.g.
intensity and spectral dependence), which is used in the identifying process.
Land cover data
The Collection 5.1 MODIS global land cover type product (MCD12C1) in 2011 is
used to identify types of anthropogenic dust sources. It includes
17 different surface vegetation types and was developed based on the data
from the International Geosphere–Biosphere Programme (IGBP) (Friedl et al.,
2010), with a spatial resolution of 0.05∘ × 0.05∘.
It provides the dominant land cover type and the sub-grid frequency
distribution of land cover classes. In the present analysis, croplands,
grasslands, cropland mosaics, and urban are the land cover types that are
considered as sources of anthropogenic dust. In addition, urban environments
are also identified based on the data set of Global Rural–Urban Mapping
Project (GRUMP) v1 with a spatial resolution of 500 m (Schneider et al.,
2010). GRUMP is a valuable resource both for researchers studying
human–environment interactions and for users who want to address critical
environmental and societal issues. GRUMPv1 consists of eight global data sets,
namely population count grids, population density grids, urban settlement
points, urban-extent grids, land/geographic unit area grids, national
boundaries, national identifier grids, and coastlines. These components allow
the GRUMP v1 to provide a raster representation of urban areas.
Method for detecting anthropogenic dust aerosol
Recently, Huang et al. (2015) developed a new method of separating natural
dust and anthropogenic dust at the global scale using CALIPSO measurements.
They defined a schematic framework of dust sources and used vertical and
horizontal transport processes as the foundation for their approach to
discriminate anthropogenic dust from natural dust in CALIPSO data, which
proceeds in a sequence of four steps. The first step is to detect the total
dust load (both natural and anthropogenic). The second step is to determine
the source region from which the dust originates. The third step is to
determine the height of a planetary boundary layer (PBL), and the final step is to determine the proportion of dust, i.e. that subset of the total
dust within the PBL.
After the anthropogenic dust was identified by the detection method described
above, the anthropogenic dust column burden was calculated as follows. First,
we determined dust extinction coefficient from the “atmospheric volume
description”, which is used to discriminate between aerosols and clouds in
the CALIPSO Level-2 aerosol extinction profile products. Then the dust
extinction coefficients with the highest confidence levels (|CAD|≥70) (Liu et al., 2008) and quality control flags of QC = 0 or QC = 1 were
selected. The dust optical depth (DOD, τ) was calculated by integrating
CAD and QC-filtered extinction coefficient of dust aerosols over the height
of the dust layer. After calculating the global total DOD (τt)
and the anthropogenic DOD (τa) from the CALIPSO profile products
between January 2007 and December 2010, the dust column burden (M) was
converted from DOD (τ), which was performed following Ginoux et
al. (2001):
M=43ρreffQextτ=1ετ,
where reff is dust effective radius, ρ is dust density,
Qext is dust extinction efficiency, and ε is mass
extinction efficiency. The formula also referred empirical values from Ginoux
et al. (2012) and assume reff=1.2 µm, ρ=2600 kg m-3, Qext=2.5, and ε=0.6 m2 g-1. This method not only modifies the maximum standard
technique developed by Jordan et al. (2010), its derived dust column burden
also has a correlation coefficient of 0.73 with the ground-based lidar
observation at the Semi-Arid Climate and Environment Observatory of Lanzhou
University (SACOL) (Huang et al., 2008; Guan et al., 2009; Liu et al., 2014),
indicating its effectiveness in detecting anthropogenic dust.
Results
Anthropogenic dust emission over global<?xmltex \hack{\break}?> semi-arid regions
Figure 1 shows the global distribution of semi-arid regions along with the
mean anthropogenic dust column burden from 2007 through 2010, demonstrating
the wide spread of anthropogenic dust. Most of the areas with high
anthropogenic dust loading are located in the middle to high latitudes of the
Northern Hemisphere, such as North China, Mongolia, northern India, central
western North America, and Sahel. The highest values are generally
distributed throughout East China and India. Note that the Northern
Hemisphere has much more anthropogenic dust than the Southern Hemisphere.
Therefore, we select four geographical regions that encompass semi-arid
regions and are influenced by anthropogenic dust in order to quantify the
recent changes. These regions marked in Fig. 1 include East China, India,
North America, and North Africa. From a visual inspection of the overlap
between the anthropogenic dust distribution and the semi-arid regions, it can
be seen that most semi-arid regions coincide with regions of high
anthropogenic dust. However, the anthropogenic dust column burdens are
different over the selected semi-arid regions: East China and India appear to
have greater amounts of anthropogenic dust than North America and North
Africa.
Global distribution of mean anthropogenic dust column burden
(g m-2) from 2007 to 2010. The grey hatching indicates semi-arid
regions.
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Figure 2 displays the total global anthropogenic dust column burden as a
function of climatological annual AI during the period of 1948–2004. The
mean AI varies from 0.0 to a maximum of 2.0. Note that the intervals in this
figure are non-uniform because they are from the classification standard for
different types of regions based on the AI, as defined in Sect. 2. Semi-arid
region is the transition zone between arid and semi-wet regions; it is
defined as the area where precipitation is less than potential evaporation
and is characterized by high temperatures (30–45 ∘C) during the
hottest months. According to Huang et al. (2016a), the annual mean
precipitation in semi-arid regions ranges from 250 to 500 mm yr-1 and
the AI of semi-arid region is between 0.2 and 0.5. The global semi-arid regions
in Fig. 2 exhibit relatively high peaks in the anthropogenic dust column
burden, with AI values ranging between 0.2 and 0.5. These semi-arid regions also experienced enhanced warming in the period of 1901–2009. (Huang et al., 2012).
Total global anthropogenic dust (AD) column burden (Tg) as a function of
the climatological mean aridity index (AI).
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Figure 3 compares the anthropogenic dust column burdens in summer (blue),
spring (green), autumn (red), and winter (black) as a function of the
climatological mean AI. The curves are similar in all four seasons, and the
anthropogenic dust column burden exhibits a dominant peak in semi-arid
regions in all four seasons, with values much larger than those in the other
regions. For the semi-arid regions, the total anthropogenic dust column
burden is the greatest in summer, followed by spring, autumn, and winter,
which may relate with the different frequency of human activities (Huang et
al., 2015), such as the construction activity is likely to be greater in
summer.
Comparison of the global anthropogenic dust (AD) column burden (Tg) in
spring (green), summer (blue), autumn (red), and winter (black) as a function
of the climatological mean aridity index (AI).
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In order to illustrate the key role of anthropogenic dust in generating dust
aerosols in the semi-arid regions, we compared the dust column burdens
corresponding to natural with mixed dust (natural and anthropogenic dusts) in
the semi-arid regions of the globe, North America, East China, North Africa,
and India, in Fig. 4. It is evident that mixed dust aerosol column burden is
greater than the pure natural dust of the globe. Both mixed and pure natural
dust column burdens are the greatest in India, followed by North Africa and
East China. The mixed dust burden of North America is a little less than that
of the natural dust. Among these regions where the mixed dust is greater than
natural dust, the difference between mixed dust and natural dust is the
largest in North Africa, followed by India and East China. For the mixed dust
aerosol, the dust column burdens of natural and anthropogenic dusts are
presented separately in Fig. 5. It shows that the anthropogenic dust column
burden is greater than that of natural dust. Additionally, the highest value of
anthropogenic dust column burden is in India, followed by North Africa, East
China, and North America; among these regions, the natural dust burden is the
highest in North Africa, followed by India, North America, and East China.
Mean dust column burdens (g m-2) of mixed dust (red) and
natural dust (blue) in the global and four geographical semi-arid regions.
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Table 1 reports the detailed values of the annual mean anthropogenic and
natural dust column burden from mixed dust areas over the semi-arid regions
of East China, India, North America, and North Africa. In the semi-arid
regions of India, the mean anthropogenic dust column burden is
0.38 g m-2 and the natural dust column
burden is 0.14 g m-2; therefore, the percentage of anthropogenic dust
is 73% of the mixed dust aerosols. The anthropogenic dust values of North
Africa, East China, and North America are 0.21, 0.18, and 0.14 g m-2
respectively. The natural dust column burdens of North Africa, East China, and
North America are 0.20, 0.02, and 0.02 g m-2 respectively, whereas the
proportions of anthropogenic dust to mixed aerosol in these three regions are
51, 90, and 87.5 % respectively. Therefore, the value of anthropogenic
contribution in India is the greatest, much more than the other three
selected regions.
Mean anthropogenic (red) and natural (blue) dust column burdens
(g m-2) from mixed dust regions in the four geographical semi-arid
regions.
![]()
Mean dust column burdens (g m-2) in four geographical semi-arid
regions.
Region
Anthropogenic
Natural
dust
dust
East China
0.18
0.02
India
0.38
0.14
North America
0.14
0.02
North Africa
0.21
0.20
Population variance in the semi-arid regions
Figure 6 is the distribution of mean population density. The population
density in semi-arid regions exhibits dramatic regional variability. For the
four selected semi-arid regions, both India and East China have higher
population densities, most semi-arid regions of North Africa have relatively
lower population density, and the population density in the semi-arid region
of North America is the lowest. The regional difference of population
indicates that influences of human activities are not uniformly distributed in the
semi-arid areas. Figure 7 illustrates the global distribution of population
change between 1990 and 2010. India exhibits the most obvious population
change, followed by North Africa and East Asia. North America exhibits an
obvious difference between east and west areas, a similar spatial pattern of
population change occurred in China. The difference between these respective
western and eastern areas may be related to their economic status. The
eastern areas of both North America and China are more industrialized than
their western counterparts. In a comparison of Figs. 6 and 7, the inconsistent
distribution between population density and population change reveals that
the regions with the higher population densities do not always have the more
obvious population change. Population density and change are related to
various factors, such as population policies, economic development status, and
political divisions.
Global distribution of mean population density (persons km-2).
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Global distribution of mean population change (persons km-2).
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Figure 8 compares the mean population density and change in the four selected
regions; it is apparent that India has the highest population density, which
reaches almost 290 persons km-2. For the other regions, population
densities from high to low are North Africa, East China, and North America.
Population change appears to be the highest in India as well, followed by
North Africa, East China, and North America. More detailed population density
and population change are illustrated in Table 2. It shows that India has the
highest population density of 290 persons km-2 with a population
increase of 80 persons km-2. The second largest population density is
North Africa. It has a population of 53 persons km-2, with a
population growth of 22 persons km-2. The population densities of
East China and North America are 49 and 22 persons km-2
respectively; the population changes in East China and North America are
8 and 6 persons km-2 respectively.
Mean population density (persons km-2) (red) and population change (persons km-2) (blue) in the
four geographical semi-arid regions.
![]()
Mean population density/change (persons km-2) in four
geographical semi-arid regions.
Region
Mean population
Mean population
density
change
East China
49.18
8.15
India
290.07
79.69
North America
22.05
5.62
North Africa
52.73
21.85
Relationship between anthropogenic dust and population density/change
Figure 9 is the mean anthropogenic dust column burden as a function of
population density. The population varies from 0 to 400 persons km-2
on the x axis with non-uninform intervals, and the mean anthropogenic dust
ranges from 0.15 to 0.35 g m-2. The anthropogenic dust shows an
increase from the population density of greater than 100 persons km-2 and illustrates that high population density greater than 100 persons km-2 has significant effect on anthropogenic dust production. The
standard deviation of anthropogenic dust is the highest for population
greater than 400 persons km-2 and the lowest for population of
25–50 persons km-2. Basically, the standard deviation of
anthropogenic dust is larger for high population density. The positive
correlation indicates increasing population density may contribute to the
production of the anthropogenic dust column burden. Figure 10 is the mean
anthropogenic dust as a function of population change. The anthropogenic dust
shows obvious increase from the population change that is greater than
25 persons km-2, with a high standard deviation. The positive
correlation reveals that the anthropogenic dust increase by population change
tends to occur in the case of large population change and confirms the
positive contribution from high population increase to production of
anthropogenic dust in the semi-arid regions.
Mean anthropogenic dust (AD) column burden (g m-2) changes as a function of
population density (persons km-2).
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Mean anthropogenic dust (AD) column burden (g m-2) changes as a function of
population change (persons km-2).
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Different land cover areas (km2).
Region
Urban area
Grasslands area
Croplands area
Cropland mosaics
East China
0.23 × 105
13.67 × 105
0.94 × 105
0.04 × 105
India
0.41 × 105
0.08 × 105
5.92 × 105
0.73 × 105
North America
1.16 × 105
13.51 × 105
1.92 × 105
0.13 × 105
North Africa
0.08 × 105
7.64 × 105
2.81 × 105
6.35 × 105
In the semi-arid regions, four typical land covers in semi-arid regions are
urban, grassland, cropland, and croplands mosaics. Figure 11 shows the global
mean anthropogenic dust column burden in semi-arid region as a function of
population density over cropland (blue line), cropland mosaics (which are
lands with a mosaic of croplands less than 60 % of the landscape
according to Friedl et al., 2002; green line), urban (red line), and
grassland (orange line). For population density less than 90 persons km-2, the anthropogenic dust burden over different land covers all shows
subtle changes. However, when the population density is larger than
90 persons km-2, the anthropogenic dust exhibits an obvious increase
as the population density increases. The anthropogenic dust increases the
fastest in the croplands (blue line), followed by crop mosaics, urban, and
grassland. Different variability of anthropogenic dust as a function of
population density over different land covers indicates that sensitivities of
anthropogenic dust to population are quite different over four typical land
covers.
Global mean anthropogenic dust (AD) column burden (g m-2) as a
function of population density (persons km-2) in semi-arid regions of
croplands (blue), croplands mosaics (green), urban (red), and grasslands
(orange).
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Different land cover area percentage (%).
Region
Urban
Grasslands
Croplands
Cropland mosaics
East China
1.56
91.86
6.29
0.29
India
5.78
1.11
82.85
10.27
North America
6.96
80.75
11.51
0.79
North Africa
0.50
45.22
16.66
37.62
The percentage of different type of land cover in the semi-arid regions
of East China, India, North America, and North Africa is illustrated in
Fig. 12a–d; they show that the components of cropland, grassland, urban, and
cropland mosaics are quite different. In the four selected regions, the
Indian semi-arid region is dominated by croplands, which has an area of
5.92 × 105 km2 (Table 3) and takes up 82.85 % of
total area (Table 4). The areas of croplands in East China, North America, and
North Africa are 0.94 × 105, 1.92 × 105, and
2.81 × 105 km2 respectively and the corresponding
percentages of croplands in East China, North America, and North Africa are
6.29, 11.51, and 16.66 % respectively. From both area and percentage, the
croplands in India are more than in the other regions. The cropland mosaics
have the largest area in North Africa (6.35 × 105 km2),
followed by India (0.73 × 105 km2), North America
(0.13 × 105 km2), and East China
(0.04 × 105 km2); their percentages are 37.62, 10.27,
0.79, and 0.29 % respectively. For grassland, it has the largest area in
East China (13.67 × 105 km2), followed by North America
(13.51 × 105 km2), North Africa
(7.64 × 105 km2), and India
(0.08 × 105 km2), with percentages of 91.86, 80.75,
45.22, and 1.11 % respectively. The urban area is the largest in North
America (1.16 × 105 km2), followed by India
(0.41 × 105 km2), East China
(0.23 × 105 km2), and North Africa
(0.08 × 105 km2), and their percentages are 6.96, 5.78,
1.56, and 0.50 % respectively.
Percentage of different types of land cover in semi-arid regions of
East China (a), India (b), North America (c), and
North Africa (d).
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Figure 13a–d illustrate the anthropogenic dust probability distributions are
quite different in East China, India, North America, and North Africa with
intervals of population and dust column burden are 20 persons km-2
and 0.05 g m-2. In these different regions, the semi-arid regions in
India have the highest anthropogenic dust in the population density of
200–250 persons km-2, and its anthropogenic dust column burden is
concentrated around 0.4 g m-2. The anthropogenic dust probability in
East Asia (Fig. 13a) and North America (Fig. 13c) show that centres of
anthropogenic dust are between 0.1 and 0.2 g m-2 and the population
density is between 0 and 30 persons km-2. Figure 13d is the anthropogenic
dust in North Africa. The highest anthropogenic dust in North Africa is
around 0.2 and 0.3 g m2, and the population density concentrated
around 0–30 persons km-2.
Anthropogenic dust (AD) probability distribution in different population
density (persons km-2) and AD column burden (g m-2) value in semi-arid regions of East
China (a), India (b), North America (c), and North
Africa (d).
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The comparison in Fig. 13 highlights the representative relationship between
anthropogenic dust and population in India, and Fig. 14 shows that quantified
influences of population on anthropogenic dust probability in typical
croplands of Indian semi-arid regions with intervals of population
density/change are 20 persons km-2. Figure 14a and b show normal
distribution of anthropogenic dust as a function of population
density/change. The population density and population change reach the
highest anthropogenic dust probability at the values of 220 and 60 persons km-2 respectively. Figure 14c and d show the both the impact from
population density and change on anthropogenic dust probability and show that
the highest peak of anthropogenic dust probability is located in the population
density of 220 persons km-2 and population change of 60 persons km-2. The shape of the 2-D and 3-D figures (Fig. 14c–d) illustrated that the impact from
population does not always have a positive contribution to the production of
anthropogenic dust, and overly excessive population will suppress the
increase of anthropogenic dust. Meanwhile, the relationship in croplands of
Indian semi-arid regions performs a direct influence of human activities on
environment change. Moreover, as the total dust aerosol in India has been
greatly increased by anthropogenic dust aerosol, it has changed the radiative
effect of dust aerosol and the radiative balance as well. Eventually, it will
contribute to regional climate change, if it does not already. Therefore, the
relationship shown in Fig. 14 has quantified the influence of human
activities on regional climate for croplands in semi-arid regions.
Anthropogenic dust (AD) probability as a function of population
density (persons km-2) (a), population change (persons km-2) (b), 2-D (c), and
3-D (d) of AD probability distribution as a function of population
density and change in typical cropland-dominated semi-arid regions in India.
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Summary and discussion
In this paper, we focused on the relationship between anthropogenic dust and
population. It was found that the total anthropogenic dust column of globe
exhibited an obvious peak in the semi-arid regions, which were much higher
than it in the other regions. Four geographical semi-arid regions of East
China, India, North America, and North Africa were chosen as our study areas
according to their anthropogenic dust levels and population. Both population
density and population change were correlated with anthropogenic dust,
indicating that these population features had effects on the production of
anthropogenic dust column burden in these semi-arid regions. In particular,
typical croplands in the Indian semi-arid region showed a normal relationship
between anthropogenic dust with population density/change; the relationship
indicated the influence of human activities on environment can be quantified
in the process of climate change. Additionally, it also proposed a typical influence
of human activities on anthropogenic dust in cropland.
Dust aerosols exert a key impact on regional radiative forcing over
semi-arid regions (Huang et al., 2006b) and are closely related to local
climate change (Guan et al., 2015b). Historical statistics revealed that
population change occurs in parallel with economic growth and with increases
in energy consumption, GHG emission, and anthropogenic dust. Further studies
are needed to gain a better understanding of the influence of anthropogenic
dust aerosols on climate change in semi-arid regions. Under the current
dynamic economic conditions throughout the world, there are still many
developing countries in semi-arid regions that are undergoing extensive
economic development or are in the process of transforming from an extensive
economic mode to an intensive economic model. Developing countries exhibit
high rates of population growth, which must be considered when forming
economic development strategies. In the developed countries, population
change may also result in increased consumption, higher energy demands, and
enhanced GHG production. Therefore, further investigations into the
influence of human activities on anthropogenic dust aerosol production and
the consequent impacts on regional climate change in semi-arid regions are
needed, with an emphasis on understanding the feedback between regional
climate change and societal development with the intent to apply more
reasonable policies in the process of economic development.