Search

With the acceleration of urbanization, industrial prosperity and economic development, China has attracted great attention as one of the largest sources of nitrogen dioxide (NO2) pollution in Asia. Many studies have carried out on the source analysis, spatial and temporal distribution, physical and chemical properties of pollutants. However, there are a few studies on the high-resolution spatial distribution of atmospheric pollutants in small-scale cities, especially by using land use regression (LUR) model. LUR model is a common method for predicting the spatial variation of pollutants. It expresses spatially local changes in the results through potential variables in the equation, thus fully reflecting the spatial differentiation of small-scale pollutant concentrations. In our study, along with 40 types potential predictor, we used LUR model to simulate the concentration of NO2 in Xi'an and to generate a high-resolution spatial distribution map of the concentrations, to provide better support for health risk assessment. In addition to using traditional predictors, due to the different classifications of roads in our study area, the use of road length as a predictor did not reflect traffic volumes well. For this reason, we weighted the roads with grades and calculated their area to fit the model. The results show that the fitting effect of the model in this study is good (adjusted R2 > 0.85), and the deviation (

With the rapid and extensive development of industry and agriculture, the soil environment inevitably becomes contaminated with heavy metals, thus creating adverse environmental conditions for flora and fauna. The traditional methods for combining field sampling with laboratory analysis of soil heavy metals are limited not only because they are time-consuming and expensive, but also because they are unable to obtain adequate information about the spatial distribution characteristics of heavy metals in soil over a large area. Three hundred and ninety-four soil samples (Gobi and farmland) were collected in an arid area in Jiuquan in Northwest China and analyzed for elements concentrations. Based on these measured concentrations, as well as rapid and environmentally friendly remote sensing (multi-spectral data), stepwise multiple linear regression (SMLR) and partial least-squares regression (PLS) were combined to predict concentrations and distributions of heavy metals in the soils of the study area. Furthermore, laboratory data were used to assess the accuracy of the prediction results. Obtained results suggest that the SMLR and PLS models were able to predict the metals contents in the study area. The concentrations of Cr, Ni, V and Zn could be predicted by two regression models, while those of Cu and Mn were predicted more accurately when they were attached to the SMLR model. The spatial distribution of heavy metals derived from the two models is consistent with measured values, indicating that it is reasonable to predict the concentrations of heavy metals in the soil of the study area using the multi-spectral data.

Four main dust sources and dust events that affected the Hexi Corridor were defined, and the HYSPLIT model was used to trace the dust that originated during the dust episodes of 2015-2017 and to quantify the contributions of dust sources to PM10. On this basis, an algorithm that quantified the contribution of dust sources to PM10 was proposed in this study. The results showed that the main dust sources affecting the Hexi Corridor are generally located in the northern part of Xinjiang, which is mainly dominated by the Gurbantunggut Desert (source A); the Taklimakan and Kumtag Deserts and their surrounding areas (source B); both Qaidam Basins (source C); and the Badain Jaran Desert, Tengger Desert, Hobq Desert, Ulan Buh Desert, and Mu Us Sandy Land and their surrounding areas (source D). The occurrence time of dust and the frequency of PM10 exceeded the daily concentration standards and showed significant characteristics of being high in the spring and low in the autumn. The higher concentration of PM10 in the winter was mainly due to anthropogenic sources from heating process. The contribution of source area D to PM10 concentration was the greatest (42%). Source area B was one of the main dust sources (with a contribution rate of 23%); however, approximately 63% of the dust in this area originates from the Kumtag Desert. The contribution of source area A is lower than that of the study area due to greater precipitation and higher vegetation coverage (22% contribution rate). Source area C has the lowest contribution to the research area due to obstruction by the Qilian Mountain (13% contribution rate).

Due to high-speed and extensive economic development, increasing urbanization, and booming industry in China, there is a growing public concern over PM2.5 pollution. The identification of possible sources of PM2.5 is a basis for studying air pollution. In this paper, the contribution of PM2.5 from both combustion and non-combustion sources is calculated using the selected tracers of SO2, CO and NO2. Changes in the characteristics of natural and anthropogenic PM2.5 sources were analyzed by combining the use of satellite imagery and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The results showed that the PM2.5 in Xi'an and Lanzhou originated from combustion sources, the contribution of non-combustion sources increased from east to west, high values appeared in Zhangye and Jiuquan (over 70%). In Lanzhou, Zhangye and Jiuquan, PM2.5 was mainly from anthropogenic sources during non-episode periods, and from natural sources (namely, dust storms) during episode days, and the proportion from natural sources increased from east to west. In addition, in our research, although the sources of PM2.5 in the four cities are different, they all face the problem of excessive PM2.5 concentration, and its pollution control problem is worth studying.

Rapid and extensive social and economic development has caused severe soil contamination by heavy metals in China. The spatial distribution, pollution levels, and health risks of metals were identified in an oasis-desert zone of northwest China. The mean concentrations of six heavy metals exceeded their corresponding background contents, and each metal concentration in farmland samples was higher than that in Gobi samples. Moreover, these heavy metals followed a similar spatial pattern and showed significant positive correlations with each other, indicating that they have the same sources. The contamination features of heavy metals and ecological risks were calculated using several quality indicators, and their health risks for population groups were quantified. The results showed that the Gobi and farmland soils were uncontaminated to moderately contaminated by heavy metals, and that farmland pollution was more serious than that of Gobi. The Gobi and farmland soils posed low ecological risks. As a whole, the non-carcinogenic risk which was caused by heavy metals was low for local residents, and the carcinogenic risk was within an acceptable level. Comparatively speaking, children were the more vulnerable population to health risks. The Zn and Cu pollution was relatively serious, and Cr and V were major contributors to health risks. Graphical abstract ᅟ.

Vegetation cover is a commonly used indicator for evaluating terrestrial environmental conditions, and for revealing environmental evolution and transitions. Spatiotemporal variations in the vegetation cover of the Hexi Corridor and surrounding areas from 2000 to 2010 were investigated using MODIS NDVI data, and the causes of vegetation cover changes were analyzed, considering both climatic variability and human activities. The vegetation cover of the study area increased during 2000–2010. The greenness of the vegetation showed a significant increase from the northwest to the southeast, which was similar to the spatial distribution of the annual precipitation. Variations in vegetation have a close relationship with those in precipitation within the Qilian Mountains region, but the NDVI is negatively correlated with precipitation in oasis areas. Increasing temperatures led to drought, inhibiting vegetation growth in summer; however, increasing temperatures may have also advanced and prolonged the growing periods in spring and autumn. The NDVI showed a slight degradation in March and July, primarily in the Qilian Mountains, and especially the Wushao Mountains. In March, due to low temperatures, the metabolism rate of vegetation was too slow to enable strong plant growth in high elevations of the Qilian Mountains. In July, increasing temperatures enhanced the intensity of transpiration and decreasing precipitation reduced the moisture available to plants, producing a slight degradation of vegetation in the Qilian Mountains. In May and August, the NDVI showed a significant improvement, primarily in the artificial oases and the Qilian Mountains. Abundant precipitation provided the necessary water for plant growth, and suitable temperatures increased the efficiency of photosynthesis, resulting in a significant improvement of vegetation in the Qilian Mountains. The improvement of production technologies, especially in irrigation, has been beneficial to the growth of vegetation in oasis areas. The implementation of large-scale vegetation management has led to several beneficial effects in the artificial oases and grasslands of the Qilian Mountains.

Spatiotemporal variations of SO2, CO, NO2, O3, PM2.5, and PM10 concentrations and their associated sources in three provincial capitals of northern China from 2014 to 2015 were examined to reveal the association between heating seasons and criteria air pollutants. Air pollutant concentrations during the heating season were significantly higher than those during the non-heating season, with the exception of O3. Moreover, the heating season with and without dust storm events was divided into aerosol season and windstorm season respectively. During aerosol season, fossil fuel-based industry and motor vehicle emissions respectively account for a major contribution to particulate matters and gaseous pollutants, and the Chinese Ambient Air Quality Standard (CAAQS) attaining rates of these pollutants exhibited the following order: PM2.5 CO > SO2 > O3. The findings of this study are helpful in understanding the influence of anthropogenic and natural activities during heating season, which may improve the effectiveness of tackling air pollution in a broader way.

Understanding the trend of vegetation change and its reaction to climate variation is important for revealing the mechanism of ecosystem behavior. However, current research rarely systematically analyzes the time effects of climate variation on vegetation dynamics (time-lag and time-accumulation effects), especially in arid and semi-arid mountainous terrain. The typical mountainous terrain—the Qilian Mountains was taken as the study area, and the spatiotemporal changes and vertical zonality distributions of the normalized vegetation index (NDVI) were explored. This study explored the time-lag and time-accumulation effects of the NDVI response to climate factors (precipitation, temperature), identified the main controlling factors that influence the variation of NDVI. The results show that in the growing season from 2000 to 2019, the NDVI represented an overall upward trend, especially in the northwest, and the growth rate of NDVI at low-altitude was greater. The time-accumulation effect of precipitation has an obvious effect on vegetation, especially on deserta and meadow; and the time-lag and time-accumulation effects of temperature have an obvious influence. Regarding the climate-vegetation response mechanism, this study finds that considering the optimal time effect is of great significance. In addition, compared with precipitation, the temperature has a more significant promotion effect on vegetation growth in the Qilian Mountains. The above results indicate that when the existing climate models study vegetation-climate interactions, considering the time effects of vegetation response to climate is of great significance for accurately monitoring vegetation dynamics under environmental changes.

Mountain ecosystems are highly susceptible to climate change. The climate tends to be warm and wet in the arid mountains of China because of the intensified water cycle under global warming, and the vegetation is undergoing profound changes. However, how vegetation phenology responds to climate change is not been entirely understood. Using MODIS-NDVI during 2000–2019, we analyzed the spatiotemporal variations in the SOS, EOS, and LOS in the Qilian Mountains (QLMs), China. These parameters were extracted using four methods, and geographic detector model was used to quantify the explanatory power of climatic factors on phenological change. The results showed that the SOS was advanced by an average of 0.26 d/yr, the EOS was delayed by an average of 0.12 d/yr, and the LOS was prolonged by an average of 0.38 d/yr during 2000–2019. The phenological characteristics of different vegetation types differed in their sensitivities to climatic factors, and the sensitivity of grassland to climatic factors was higher than those of other vegetation types. The preseason temperature and sunshine duration contributed more to SOS and EOS changes of forests than that of other vegetation types. Moreover, the SOS was more sensitive to preseason precipitation, and it was the main SOS determinant across grassland and meadows. The EOS was more sensitive to the daily minimum temperature. More importantly, climatic factors did not act independently on vegetation phenological changes. The interactions between preseason precipitation and temperature and between preseason sunshine duration and temperature had significantly affected the changes in the SOS and EOS, respectively. The results of this study highlight the response of different vegetation types to climate change in the arid mountainous areas, which is significant for improving the performance of phenology models.

To investigate the spatial and temporal behaviors of particulate matter in Lanzhou, Jinchang and Jiayuguan during 2014, the hourly concentrations of PM2.5 and PM10 were collected from the Ministry of Environmental Protection (MEP) in this study. The analysis indicated that the mean annual PM10 (PM2.5) concentrations during 2014 were 115 ± 52 μg/m3 (57 ± 28 μg/m3), 104 ± 75 μg/m3 (38 ± 22 μg/m3) and 114 ± 72 μg/m3 (32 ± 17 μg/m3) in Lanzhou, Jinchang and Jiayuguan, respectively, all of which exceeded the Chinese national ambient air quality II standards for PM. Higher values for both PM fractions were generally observed in spring and winter, and lower concentrations were found in summer and autumn. Besides, the trend of seasonal variation of particulate matter (PM) in each city monitoring site is consistent with the average of the corresponding cities. Anthropogenic activities along with the boundary layer height and wind scale contributed to diurnal variations in PM that varied bimodally (Lanzhou and Jinchang) or unimodally (Jiayuguan). With the arrival of dust events, the PM10 concentrations changed dramatically, and the PM10 concentrations during dust storm events were, respectively, 19, 43 and 17 times higher than the levels before dust events in Lanzhou, Jinchang and Jiayuguan. The ratios (PM2.5/PM10) were lowest, while the correlations were highest, indicating that dust events contributed more coarse than fine particles, and the sources of PM are similar during dust storms. The relationships between local meteorological parameters and PM concentrations suggest a clear association between the highest PM concentrations, with T ≤ 7 °C, and strong winds (3-4 scale). However, the effect of relative humidity is complicated, with more PM10 and PM2.5 exceedances being registered with a relative humidity of less than 40% and 40-60% in Lanzhou, while higher exceedances in Jinchang appeared at a relative humidity of 80-100%.

Air pollution events occur frequently in northwest China, which results in serious detrimental effects on human health. Therefore, it is essential to understand the air pollution characteristics and assess the risks to humans. In this study, we analyzed the pollution characteristics of criteria pollutants in six key cities in northwest China from 2015 to 2018. We used the air quality index (AQI), aggregate AQI (AAQI), and health-risk based AQI (HAQI) to assess the health risks and determine the proportion of people exposed to air pollution. Additionally, on this basis, the AirQ2.2.3 model was used to quantify the health effects of the pollutants. The results showed that PM10 pollution occurred mainly in spring and winter and was caused by frequent dust storms. PM2.5 pollution was caused mainly by anthropogenic activities (especially coal-fired heating in winter). Because of a series of government policies and pollutant reduction measures, PM2.5, SO2, NO2, and CO concentrations showed a downward trend during the study period (except for a small increase in the case of NO2 in some years.). However, O3 showed high concentrations due to the high intensity of solar radiation in summer and inadequate emission reduction measures. The air quality levels based on their classification were generally higher than the Chinese ambient air quality standard classified by the AQI index. We also found that the higher the AQI index was, the more serious the air pollution classified based on the AAQI and HAQI indices was. The HAQI index could better reflect the impact of pollutants on human health. Based on the HAQI index, 20% of the population in the study area was exposed to polluted air. The total mortality values attributable to PM10, PM2.5, SO2, O3, NO2, and CO, quantified by the AirQ2.2.3 model, were 3.00%, 1.02%, 1.00%, 4.22%, 1.57%, and 0.95% (Confidence Interval:95%), respectively; the attributable proportions of mortality for respiratory system and cardiovascular diseases were consistent with the change rule of total mortality, because the number of deaths attributable to the latter was greater than that for the former. According to the exposure reaction curves of pollutants, PM10 and PM2.5 still showed a large change at high concentrations. However, the tendencies of SO2, NO2, CO, and O3 were more obvious under low concentration exposure, which indicated that the expected mortality rate due to lower air pollution concentrations was much higher than the mortality due to high air pollution concentrations.

Northern China is a significant source of dust source in Central Asia. Thus, high-resolution analysis of dust storms and comparison of dust sources in different regions of northern China are important to clarify the formation mechanism of East Asian dust storms and predict or even prevent such storms. Here, we analyzed spatiotemporal trends in dust storms that occurred in three main dust source regions during 1960–2007: Taklimakan Desert (western region [WR]), Badain Jaran and Tengger Deserts (middle region [MR]), and Otindag Sandy Land (eastern region [ER]). We analyzed daily dust storm frequency (DSF) at the 10-day scale (first [FTDM], middle [MTDM], and last [LTDM] 10 days of a month), and investigated the association of dust storm occurrences with meteorological factors. The 10-day DSF was greatest in the FTDM (accounting for 77.14% of monthly occurrences) in the WR, MTDM (45.85%) in the MR, and LTDM (72.12%) in the ER, showing a clear trend of movement from the WR to the ER. Temporal analysis of DSF revealed trend changes over time at annual and 10-day scales, with mutation points at 1985 and 2000. We applied single-factor and multiple-factor analyses to explore the driving mechanisms of DSF at the 10-day scale. Among single factors, a low wind-speed threshold, high solar radiation, and high evaporation were correlated with a high DSF, effectively explaining the variations in DSF at the 10-day scale; however, temperature, relative humidity, and precipitation poorly explained variations in DSF. Similarly, multiple-factor analysis using a classification and regression tree revealed that maximum wind speed was a major influencing factor of dust storm occurrence at the 10-day scale, followed by relative humidity, evaporation, and solar radiation; temperature and precipitation had weak influences. These findings help clarify the mechanisms of dust storm occurrence in East Asia.

Heavy metals in agricultural soil receive much attention because they are easily absorbed by crop into the ecosystem. Managing the discharge of heavy metals from the source is an effective way to prevent and control heavy metals pollution. Grouped principal component analysis (GPCA) and Positive Matrix Factorization (PMF) receptor models were utilized in this study to conduct source apportionment, and the former was optimal because of the accuracy of predicting. Based on the source contribution by GPCA/APCS, heavy metals were evaluated by fuzzy synthetic evaluation model and health risk assessment model. The results of source apportionment showed that heavy metals in Zhangye agricultural soil were mainly affected by steel industry, traffic, agrochemicals, manures, mining activities, leather industry and metal processing industry source. Fuzzy synthetic evaluation showed that the pollution levels of Chromium (Cr) derived by leather industry and metal processing industry and Nickel (Ni) derived by steel industry and traffic source were higher. Health risk assessment revealed that the non-carcinogenic and carcinogenic risks of Cr derived by leather industry and metal processing industry and Lead (Pb) derived by steel industry and traffic source were higher.

The evolution of vegetation patterns plays a crucial role in monitoring ecosystem dynamics in face of global warming. Notably, the time-series vegetation trends vary unevenly over time. However, conventional linear methods based on the stationarity assumption, cannot detect the implicit characteristics of nonlinear and gradual vegetation changes. Meanwhile, characterizing the inherent features of nonlinearity and nonstationarity of climatic drivers remains a challenge. This study applied multidimensional ensemble empirical mode decomposition (MEEMD) and Breaks For Additive Seasonal and Trend (BFAST) algorithm to diagnose spatiotemporal evolution and abrupt change in vegetation secular trends based on normalized difference vegetation index (NDVI) data of the Hexi Corridor during 1982–2015. Geographically and temporally weighted regression (GTWR) was used to address the spatiotemporal nonlinearity and nonstationarity of climatic drivers. A wide range of browning trends gradually evolved into greening trends from 1982 to 2015. Compared with ordinary least squares regression, MEEMD could adaptively decompose short-term trends of noise and seasonality and elucidate the entire evolutionary process of NDVI trends. Browning trends prevailed before abrupt changes, and greening trends widely expanded after 2006 due to drought before and intense precipitation during this year. GTWR addressed the nonequilibrium effect of the time dimension on climate drivers, and the R-squared achieved 0.86. The response processes between vegetation and climatic drivers showed significant spatiotemporal nonstationarity and aggregation characteristics based on three-dimensional visualization. In addition to the drivers of temperature, precipitation, solar radiation, and potential evapotranspiration, the drying effect and mechanical stimuli of wind speed on vegetation could not be underestimated. This study provides a novel framework to solve nonlinearity and nonstationarity problems related to vegetation trends and their response mechanisms and promotes the application of remote sensing to solve practical problems.

Dust storms in Northwest China are a major component of global dust storms and it is important to be able to identify and quantitatively analyze their dust trajectories. In this study, the dust events, sources, and transport paths in the Hexi Corridor were identified using ground-measured PM10 data, Navy Aerosol Analysis and Prediction System (NAAPS) data, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. Based on these data, the dust trajectories were then screened. Dust events from five cities in the Hexi region were determined for 2015–2018. Next, five main areas affecting the Hexi region were defined; which included the Gobi region between Xinjiang and Mongolia (XG), Taklimakan Desert (TD), Kumtag Desert (KD), Qaidam Basin (QB), and Badain Jaran and Tengger Deserts (BJTD). We quantified the contributions of the dust trajectories for these five main areas and determined that the major areas that affect the Hexi region are the BJTD and KD, which are located close to the study area. The contribution of the dust trajectories in XG is smaller, owing to its higher vegetation coverage, greater precipitation, and larger particulate matter size. Of the five main areas that affect the Hexi region, the dust intensity in TD is the highest. However, its dust trajectory contribution to the study area is lower. This is because the dust is not elevated to the free atmosphere readily by the easterly wind, owing to the closed mountain basin location. The contribution of the dust trajectories in the QB to the study area is the lowest, mostly due to the low dust intensity in this region and the obstruction of dust trajectories by the Qilian Mountains.

The arid region of northwest China provides a unique terrestrial ecosystem to identify the response of vegetation activities to natural and anthropogenic changes. To reveal the influences of climate and anthropogenic factors on vegetation, the Normalized Difference Vegetation Index (NDVI), climate data, and land use and land cover change (LUCC) maps were used for this study. We analyzed the spatiotemporal change of NDVI during 2000–2015. A partial correlation analysis suggested that the contribution of precipitation (PRE) and temperature (TEM) on 95.43% of observed greening trends was 47% and 20%, respectively. The response of NDVI in the eastern section of the Qilian Mountains (ESQM) and the western section of the Qilian Mountains (WSQM) to PRE and TEM showed opposite trends. The multiple linear regressions used to quantify the contribution of anthropogenic activity on the NDVI trend indicated that the ESQM and oasis areas were mainly affected by anthropogenic activities (26%). The observed browning trend in the ESQM was attributed to excessive consumption of natural resources. A buffer analysis and piecewise regression methods were further applied to explore the influence of urbanization on NDVI and its change rate. The study demonstrated that urbanization destroys the vegetation cover within the developed city areas and extends about 4 km beyond the perimeter of urban areas and the NDVI of buffer cities (counties) in the range of 0–4 km (0–3 km) increased significantly. In the range of 5–15 (4–10) km (except for Jiayuguan), climate factors were the major drivers of a slight downtrend in the NDVI. The relationship of land use change and NDVI trends showed that construction land, urban settlement, and farmland expanded sharply by 171.43%, 60%, and 10.41%, respectively. It indicated that the rapid process of urbanization and coordinated urban-rural development shrunk ecosystem services.

PM10 and PM2.5 concentration data were collected from five air-quality monitoring sites in Lanzhou from October 2014 to October 2015, revealing the spatial-temporal behavior of local particulate matter (PM). The Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and the PM2.5-to-PM10 ratio model were used to investigate the primary transport path, potential source areas and contributions of the East Asian sandstorm to PM in Lanzhou. The analysis in three functional areas of the city indicated that the monthly variation in PM2.5 displayed a unimodal U pattern (the highest value was during the heating period), whereas that of PM10 displayed a bimodal pattern (the primary peak appeared in the spring, and the secondary peak appeared in the winter). These two patterns originated from different PM sources. The PM2.5 was primarily affected by human activities, and the PM10 was influenced by both natural and anthropogenic activities, but the relative contributions of these activities were associated with spatial-temporal variations. The daily PM10 and PM2.5 concentration variations displayed a bimodal pattern in the three functional areas: the peak values appeared at 11:00-13:00 and 22:00-1:00, respectively, and the lowest values appeared at 4:00-6:00 and 16:00-18:00, respectively. On the monthly, seasonal and daily scales, the PM concentrations exhibited similar patterns in the industrial, urban and rural areas, indicating that they were partly controlled by the regional natural environment. Meanwhile, due to anthropogenic factors, considerable PM amounts were discharged into the external environment, leading to maximum and minimum concentrations of PM appearing in the industrial and rural areas, respectively. The HYSPLIT model showed that dust storms from the northwest desert and Gobi regions affected Lanzhou three times in March 2015 and contributed 68% and 40% of the total mass of PM10 and PM2.5, respectively.

Dust storms that occur frequently during the spring in the arid and semiarid regions of northwest China can significantly increase PM10 loadings in the local area and its surroundings, and impair environmental ecology, human activity and health. Based on the monitoring data of PM10 and backward trajectories of air parcels calculated by the Hybrid Single Particle Lagrangian Integrated Trajectory model in Jiayuguan, Jinchang and Lanzhou during the spring seasons from 2014 to 2017, trajectory cluster analysis was used to identify the major transport pathways of PM10, and concentration-weighted trajectory and trajectory sector analysis were applied to identify the potential sources and quantify regional transported contributions. The results showed that there were major transport pathways coming from three directions: the northwestern pathways (for Jinchang and Lanzhou), the western pathways (for Jiayuguan and Lanzhou), and the northern pathways (for Jiayuguan and Jinchang). The corresponding potential dust sources for these pathways were the northwesterly sources, the westerly sources and the northerly sources, respectively. In addition, the northern Tibetan Plateau was another important source affecting Jiayuguan. There were distinct differences among the regional transport contributions from various directions. The PM10 concentrations (percentages) that exogenous dust sources contributed to Jiayuguan, Jinchang and Lanzhou decreased in a sequence from west to east at 47 μg/m3 (29.8%), 40 μg/m3 (27.1%) and 26 μg/m3 (17.3%), respectively, which were attributed predominantly to the long-range transport of dust sources with the remainder coming from local or nearby emissions. The above decreasing tendency was in accordance with the distance effect from the dust sources to the receptor sites.

Lanzhou has been one of the most seriously polluted cities in the world due to its special geographical location, weather conditions and large amount of emissions from coal-fired factories. These factors make it an ideal place for urban particulate pollution study. In this study, a set of environmental magnetic parameters (χlf, χfd%, χARM, saturation isothermal remanent magnetization (SIRM), HIRM, SOFT and backfield IRM) were measured on dustfall samples collected monthly during April 1997–May 2000 and November 2004–October 2005 at the Lanzhou University campus (KLD). Results show that the magnetic assemblage in the dustfall is dominated by pseudo-single domain (PSD) magnetite associated with maghaemite and haematite. Anthropogenic activities (mainly from coal burning) are the main sources for urban particulate pollutants in Lanzhou. The great efforts of the government and local authority in reducing local industrial and domestic emissions during the past several decades has led to improved air quality in winter in Lanzhou; however, the air quality in summer has not been significantly improved.