Temporal-spatial analysis of crop residue burning in China and its impact on aerosol pollution

Yu, M. H.; Yuan, Xiaolei; He, Qingqing; Yu, Yuhan; Cao, Kai; Yang, Yong; Zhang, Wenting

doi:10.1016/j.envpol.2018.11.001

Cited by 26 publications

(14 citation statements)

References 24 publications

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“…It has been observed that AOT has increased by 40% over several areas of India from 2000 to 2009 due to an increase in urbanization, the burning of biomass and fossil fuels, and forest fires [34]. The residue burning is used as a means to clear agricultural fields [35]. The counts of residue burning can be mapped from satellite remote sensing, fire event data.…”

Section: Methodsmentioning

confidence: 99%

Trends of Aerosol Optical Thickness Using VIIRS S-NPP during Fog Episodes in Pakistan and India

et al. 2021

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Aerosol Optical Thickness (AOT) is one of the important parameters for assessing regional and global level of climate change. Fog episodes have considerably increased in south Asia because of environmental factors, and the burning of agricultural residue leads to major social and economic problems. In present study, Mann-Kendall trend analysis of AOT and active fire events was done, and their significance were assessed using long-term (October 2012–February 2020) remote sensing data derived smog maps. Visible Infrared Imaging Radiometer Suite National Polar Partnership (VIIRS N-PP) was used to map AOT episodes over the northern region of Pakistan and India. Results reveal that AOT displays a significantly decreasing trend over the northern and eastern region of Pakistan and a similar decreasing trend from the Western to Eastern region of India. Furthermore, active fire events have a significantly increasing trend at the Northern region of Pakistan. However, fire events have a significantly decreasing trend over the southern and southeastern region of India. Additionally, statistically significant decreasing trends were observed for AOT over Chakwal (p-value = 0.2, Z_MK = −2.3) and Patiala (p-value = 0.15, Z_MK = −3.2). Fire events have a significantly increasing trend for Dera Ismail Khan (p-value = 0.01, Z_MK = 1.9), Jhang (p-value = 0.01, Z_MK = 1.9), and Chakwal (p-value = 0.01, Z_MK = 1.8), while they are significantly decreasing trend near New Delhi (p-value = 0.2, Z_MK = −0.9), Aligarh (p-value = 0.15, Z_MK = −0.9) and Patiala (p-value = 0.2, Z_MK = −0.8).

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Section: Methodsmentioning

confidence: 99%

Trends of Aerosol Optical Thickness Using VIIRS S-NPP during Fog Episodes in Pakistan and India

et al. 2021

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“…As non-point sources, the emissions from agricultural sources are significantly affected by rainfall variation and human farming activities. Since the types and contribution rate of pollution sources are related to the urbanization level, and the socioeconomic structure, agricultural practices, and rural conditions are difficult to change in a short time [49,50], it is necessary to identify the pollution load of different administrative regions scale [51].…”

Section: Monthly and Seasonal Pollution Loads Of Different Pollution Sourcesmentioning

confidence: 99%

Basin-Scale Pollution Loads Analyzed Based on Coupled Empirical Models and Numerical Models

Zhang

Chen

Yang

et al. 2021

IJERPH

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Pollutant source apportionment is of great significance for water environmental protection. However, it is still challenging to accurately quantify pollutant loads at basin-scale. Refined analytical methods combined the pollution discharge coefficient method (PDCM), field observation, and numerical model (Soil & Water Assessment Tool, SWAT) to make quantitative source appointment in the Tuojiang River, a key tributary of the upper Yangtze River. The chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (N-NH4+) were analyzed. Results showed that the urban sewage treatment plant point source has the largest contribution to COD, TN, and N-NH4+, while TP is mostly from the agricultural sources throughout the year. The total inflowing loads of pollution sources are significantly affected by rainfall. The overall pollution characteristics showed that pollutant loads present in different seasons are as follows: wet season > normal season > dry season. The month with the highest levels of pollutants is July in the wet season. Among the nine cities, the city that contributes the most COD, TN and N-NH4+, is Neijiang, accounting for about 25%, and the city that contributes the most TP is Deyang, accounting for 23%. Among the sub-basins, the Fuxi River subbasin and Qiuxihe River subbasin contribute the most pollutant loads. The technical framework adopted in this paper can be used to accurately identify the types, administrative regions and sub-basins of the main pollution sources in the watershed, which is conducive to management and governance of the environment.

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“…This can be attributed to two aspects. One is the massive anthropogenic emissions and the other is the special geomorphology of basins and valleys that hinders the dissipation of pollutants [56,61]. Furthermore, southern China tends to have a relatively higher temperature throughout the year, thus facilitating stronger photochemical reaction than the north [22].…”

Section: Seasonal Variation Of Dominant Aerosol Types Over Chinamentioning

confidence: 99%

“…BB aerosols generally occur in the Sichuan Basin and NCP and the seasonal mean ratio shows an increasing trend from less than 10% in summer to over 20% in winter (Figure 7). The highest ratio of BB in winter may be attributable to three aspects: (1) Residents burn straw to heat themselves in the cold weather [62], (2) people frequently burn paper money and fireworks around the Chinese Spring Festival to cherish the memory of ancestors at a national scale [61,63], (3) BB aerosols accumulate under the stable atmosphere in winter and the adverse weather condition hinders the dissipation of BB aerosols and increases the residence time of BB aerosols. Thus, BB ratio will increase [64,65].…”

Section: Seasonal Variation Of Dominant Aerosol Types Over Chinamentioning

confidence: 99%

Spatiotemporal Distribution of Major Aerosol Types over China Based on MODIS Products between 2008 and 2017

et al. 2020

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Knowledge of aerosol-type distribution is critical to the evaluation of aerosol–climate effects. However, research on aerosol-type distribution covering all is limited. This study characterized the spatiotemporal distribution of major aerosol types over China by using MODerate resolution Imaging Spectroradiometer (MODIS) products from 2008 to 2017. Two aerosol-type classification methods were combined to achieve this goal. One was for relatively high aerosol load (AOD ≥ 0.2) using aerosol optical depth (AOD) and aerosol relative optical depth (AROD) and the other was for low aerosol load (AOD < 0.2) using land use and population density information, which assumed that aerosols are closely related to local emissions. Results showed that the dominant aerosol-type distribution has a distinct spatial and temporal pattern. In western China, background aerosols (mainly dust/desert dust and continent aerosol) dominate with a combined occurrence ratio over 70% and they have slight variations on seasonal scale. While in eastern China, the dominant aerosols show strong seasonal variations. Spatially, mixed aerosols dominate most parts of eastern China in spring due to the influence of long-range transported dust from Taklamakan and Gobi desert and urban/industry aerosols take place in summer due to strong photochemical reactions. Temporally, mixed and urban/industry aerosols co-dominate eastern China.

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Temporal-spatial analysis of crop residue burning in China and its impact on aerosol pollution

Cited by 26 publications

References 24 publications

Trends of Aerosol Optical Thickness Using VIIRS S-NPP during Fog Episodes in Pakistan and India

Trends of Aerosol Optical Thickness Using VIIRS S-NPP during Fog Episodes in Pakistan and India

Basin-Scale Pollution Loads Analyzed Based on Coupled Empirical Models and Numerical Models

Spatiotemporal Distribution of Major Aerosol Types over China Based on MODIS Products between 2008 and 2017

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