The compulsory use of dust-proof nets led to significant environmental, health and economic benefits in China

Ziyue Chen; Chaoqun Zhang; Jiayu Xu; Jie Wang; Lin Zhang; Manchun Li; Bingbo Gao; Lin Yang; Qiancheng Lv; Jing Yang; Chenghu Zhou; Qiao Wang

doi:10.59717/j.xinn-geo.2025.100160

The Innovation Geoscience > 2025 Vol. 3 > No. 4 > 100160

Next Article Previous Article

ARTICLE Open Access

Cite PDF

The compulsory use of dust-proof nets led to significant environmental, health and economic benefits in China

1.
Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China
2.
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
3.
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
4.
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
5.
These authors contributed equally

More Information

Corresponding authors: zhanglg@pku.edu.cn (L.Z.); yanglin@nju.edu.cn (L.Y.)
Received Date: December 06, 2024

Accepted Date: June 28, 2025

Published Online: August 06, 2025

The Innovation Geoscience 3(4), https://doi.org/10.59717/j.xinn-geo.2025.100160 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

DownLoad: Full size image

PUBLIC SUMMARY

PUBLIC SUMMARY
1. China mandated dust-proof nets (DPNs) on construction sites to combat air pollution.
  
  We used satellite data and machine learning to find DPNs use surged after the 2017 policy.
  
  This policy helped avoid over 26,836 premature deaths from 2017 to 2021.
  
  DPNs are a cost-effective clean air tool with great potential for wider use.

ABSTRACT

ABSTRACT

In 2017, China launched a regulation on the compulsory coverage of construction wastes with dust-proof nets (DPNs) to mitigate air pollution resulting from urban construction activities. Challenges exist in quantifying how its implementation has mitigated airborne particulate matter (PM) pollution in China. Here we developed a framework combining high-resolution satellite images, machine learning, and an air quality model to identify DPNs and associated PM mitigation across China from 2016 to 2021. The total national DPN area surged significantly since 2017 and peaked in 2019, 9.78 times of that in 2016, especially in North China Plain (NCP). With a simultaneous increase of coverage duration, DPNs caused national PM emission reduction in 2019 was 23 times larger than 2016. Based on the extracted DPN dataset, we employed WRF-Chem regional air quality model to simulate the effects of DPNs on the PM-reduction across China. Next, we calculated that the use of DPNs helped a total of 253.8 million of people exposure to notably improved air quality and 26,836 people (95% CI: 23,630-30,041) avoided premature mortalities from 2017 to 2021. Accordingly, we quantified the economic benefits from DPNs use arcoss China reached 20.7 billion based on the Value of Statistical Life (VSL) model. Large potential remains for DPN use in China. The national DPN coverage could additionally increase by 3.3 times if DPN coverage rate in NCP would transfer to the entire country.
- Dust-proof net /
- Urbanization /
- Air pollution /
- Premature mortalities /
- Economic benefits

FullText(HTML)

REFERENCES

[1]	Zheng B., Tong D., Li M., et al. (2018). Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 18:14095−14111. DOI:10.5194/acp-18-14095-2018 View in Article CrossRef Google Scholar
[2]	Xu R., Ye T., Huang W., et al. (2024). Global, regional, and national mortality burden attributable to air pollution from landscape fires: a health impact assessment study. The Lancet 404:2447−2459. DOI:10.1016/S0140-6736(24)02251-7 View in Article CrossRef Google Scholar
[3]	Singh K., Lobell D. B. and Azevedo I. M. L. (2025). Quantifying the impact of air pollution from coal-fired electricity generation on crop productivity in India. Proc. Natl. Acad. Sci. U.S.A. 122:e2421679122. DOI:10.1073/pnas.2421679122 View in Article CrossRef Google Scholar
[4]	Yue H., He C., Huang Q., et al. (2024). Substantially reducing global PM2.5-related deaths under SDG3.9 requires better air pollution control and healthcare. Nat. Commun. 15:2729. DOI:10.1038/s41467-024-46969-3. View in Article Google Scholar
[5]	Wang H., Zhou S. and Zhang P. (2024). Clean heating and clean air: Evidence from the coal-to-gas program in China. China Econ. Rev. 85:102179. DOI:https://doi.org/10.1016/j.chieco.2024.102179. View in Article Google Scholar
[6]	Wang L., Chen X., Zhang Y., et al. (2021). Switching to electric vehicles can lead to significant reductions of PM2.5 and NO2 across China. One Earth 4:1037-1048. DOI:10.1016/j.oneear.2021.06.008. View in Article Google Scholar
[7]	Huang H.N., Yang Z., Guo Y., et al. (2025). Impact of agricultural straw open-field burning on concentrations of six criteria air pollutants in China. Environ. Pollut. 373:126109. DOI:https://doi.org/10.1016/j.envpol.2025.126109. View in Article Google Scholar
[8]	Zhang Q., Zheng Y., Tong D., et al. (2019). Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl. Acad. Sci. U.S.A. 116:24463-24469. DOI:10.1073/pnas.1907956116. View in Article Google Scholar
[9]	Guo P., Yu S., Wang L., et al. (2019). High-altitude and long-range transport of aerosols causing regional severe haze during extreme dust storms explains why afforestation does not prevent storms. Environ. Chem. Lett. 17:1333−1340. DOI:10.1007/s10311-019-00858-0 View in Article CrossRef Google Scholar
[10]	Zhou L., Yuan B., Mu H., et al. (2021). Coupling relationship between construction land expansion and PM2.5 in China. Environ. Sci. Pollut. Res. 28:33669-33681. DOI:10.1007/s11356-021-13160-w. View in Article Google Scholar
[11]	Chuai X., Lu Q., Huang X., et al. (2021). China’s construction industry-linked economy-resources-environment flow in international trade. J. Clean. Prod. 278:123990. DOI:https://doi.org/10.1016/j.jclepro.2020.123990. View in Article Google Scholar
[12]	Ministry of Housing and Urban-Rural Development of the People's Republic of China. (2017). The General Office of the Ministry of Housing and Urban-Rural Development on the issuance of special treatment of construction site dust work program notice.https://www.mohurd.gov.cn/gongkai/zc/wjk/art/2017/art_17339_231276.html (accessed 13 March 2017 View in Article Google Scholar
[13]	Sun X., Yin D., Qin F., et al. (2023). Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery. Nat. Commun. 14:1444. DOI:10.1038/s41467-023-37136-1 View in Article CrossRef Google Scholar
[14]	Zhang C., Zhou L., Du M., et al. (2022). A cross-channel multi-scale gated fusion network for recognizing construction and demolition waste from high-resolution remote sensing images. Int. J. Remote Sens. 43:4541−4568. DOI:10.1080/01431161.2022.2115864 View in Article CrossRef Google Scholar
[15]	Cao Y. and Huang X. (2022). A coarse-to-fine weakly supervised learning method for green plastic cover segmentation using high-resolution remote sensing images. ISPRS J. Photogramm. Remote Sens. 188:157-176. DOI:https://doi.org/10.1016/j.isprsjprs.2022.04.012. View in Article Google Scholar
[16]	Li Z., Guo H., Zhang L., et al. (2022). Time-Series Monitoring of Dust-Proof Nets Covering Urban Construction Waste by Multispectral Images in Zhengzhou, China. Remote Sens. View in Article Google Scholar
[17]	Zhu Z., Wang S. and Woodcock C. E. (2015). Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens. Environ. 159:269-277. DOI:https://doi.org/10.1016/j.rse.2014.12.014. View in Article Google Scholar
[18]	Wang X., Xiao X., Zou Z., et al. (2020). Gainers and losers of surface and terrestrial water resources in China during 1989–2016. Nat. Commun. 11:3471. DOI:10.1038/s41467-020-17103-w View in Article CrossRef Google Scholar
[19]	Chen B., Wu S., Song Y., et al. (2022). Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. Nat. Commun. 13:4636. DOI:10.1038/s41467-022-32258-4 View in Article CrossRef Google Scholar
[20]	Lin X., Wu S., Chen B., et al. (2022). Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 194:1-20. DOI:https://doi.org/10.1016/j.isprsjprs.2022.09.016. View in Article Google Scholar
[21]	Zhang H. K. and Roy D. P. (2017). Using the 500m MODIS land cover product to derive a consistent continental scale 30m Landsat land cover classification. Remote Sens. Environ. 197:15-34. DOI:https://doi.org/10.1016/j.rse.2017.05.024. View in Article Google Scholar
[22]	Sulla-Menashe D., Gray J. M., Abercrombie S. P., et al. (2019). Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product. Remote Sens. Environ. 222:183-194. DOI:https://doi.org/10.1016/j.rse.2018.12.013. View in Article Google Scholar
[23]	Borrelli P., Robinson D. A., Fleischer L. R., et al. (2017). An assessment of the global impact of 21st century land use change on soil erosion. Nat. Commun. 8:2013. DOI:10.1038/s41467-017-02142-7 View in Article CrossRef Google Scholar
[24]	Li M., Liu H., Geng G., et al. (2017). Anthropogenic emission inventories in China: a review. Natl. Sci. Rev. 4:834−866. DOI:10.1093/nsr/nwx150 View in Article CrossRef Google Scholar
[25]	Chen Y., Zhang L., Henze D. K., et al. (2021). Interannual variation of reactive nitrogen emissions and their impacts on PM2.5 air pollution in China during 2005–2015. Environ. Res. Lett. 16:125004. DOI:10.1088/1748-9326/ac3695. View in Article Google Scholar
[26]	Li M., Zhang Q., Kurokawa J. I., et al. (2017). MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 17:935−963. DOI:10.5194/acp-17-935-2017 View in Article CrossRef Google Scholar
[27]	Guenther A., Karl T., Harley P., et al. (2006). Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys. 6:3181−3210. DOI:10.5194/acp-6-3181-2006 View in Article CrossRef Google Scholar
[28]	Wiedinmyer C., Akagi S. K., Yokelson R. J., et al. (2011). The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. 4:625−641. DOI:10.5194/gmd-4-625-2011 View in Article CrossRef Google Scholar
[29]	Li X., Gong P., Zhou Y., et al. (2020). Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environ. Res. Lett. 15:094044. DOI:10.1088/1748-9326/ab9be3 View in Article CrossRef Google Scholar
[30]	Zhang L., Yang L., Zohner C. M., et al. Direct and indirect impacts of urbanization on vegetation growth across the world’s cities. Sci. Adv. 8:eabo0095. DOI:10.1126/sciadv.abo0095. View in Article Google Scholar
[31]	Gao B., Yang J., Chen Z., et al. (2023). Causal inference from cross-sectional earth system data with geographical convergent cross mapping. Nat. Commun. 14:5875. DOI:10.1038/s41467-023-41619-6 View in Article CrossRef Google Scholar
[32]	Chen Y., Wu Y., Ma J., et al. (2021). Microplastics pollution in the soil mulched by dust-proof nets: A case study in Beijing, China. Environ. Pollut. 275:116600. DOI:https://doi.org/10.1016/j.envpol.2021.116600. View in Article Google Scholar
[33]	Zhang P., Du P., Guo S., et al. (2022). A novel index for robust and large-scale mapping of plastic greenhouse from Sentinel-2 images. Remote Sens. Environ. 276:113042. DOI:https://doi.org/10.1016/j.rse.2022.113042. View in Article Google Scholar
[34]	Zeng Y., Hao D., Huete A., et al. (2022). Optical vegetation indices for monitoring terrestrial ecosystems globally. Nat. Rev. Earth Environ. 3:477−493. DOI:10.1038/s43017-022-00298-5 View in Article CrossRef Google Scholar
[35]	Wei J., Huang W., Li Z., et al. (2020). Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches. Remote Sens. Environ. 248:112005. DOI:https://doi.org/10.1016/j.rse.2020.112005. View in Article Google Scholar
[36]	Phalke A. R., Özdoğan M., Thenkabail P. S., et al. (2020). Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 167:104-122. DOI:https://doi.org/10.1016/j.isprsjprs.2020.06.022. View in Article Google Scholar
[37]	Belgiu M. and Drăguţ L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114:24-31. DOI:https://doi.org/10.1016/j.isprsjprs.2016.01.011. View in Article Google Scholar
[38]	Zhang X., Liu L., Zhao T., et al. (2023). GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020. Earth Syst. Sci. Data 15:265−293. DOI:10.5194/essd-15-265-2023 View in Article CrossRef Google Scholar
[39]	Gorelick N., Hancher M., Dixon M., et al. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202:18-27. DOI:https://doi.org/10.1016/j.rse.2017.06.031. View in Article Google Scholar
[40]	Breiman L. (2001). Random Forests. Mach. Learn. 45:5−32. DOI:10.1023/A:1010933404324 View in Article CrossRef Google Scholar
[41]	Loh W.Y. (2011). Classification and regression trees. WIREs Data Min. Knowl. Discov. 1:14-23. DOI:https://doi.org/10.1002/widm.8. View in Article Google Scholar
[42]	Xie Y., Lark T. J., Brown J. F., et al. (2019). Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 155:136-149. DOI:https://doi.org/10.1016/j.isprsjprs.2019.07.005. View in Article Google Scholar
[43]	EPA U. S. (1995). Compilation of Air Pollutant Emission Factors, AP-42. View in Article Google Scholar
[44]	Li N., Long X., Tie X., et al. (2016). Urban dust in the Guanzhong basin of China, part II: A case study of urban dust pollution using the WRF-Dust model. Sci. Total Environ. 541:1614-1624. DOI:https://doi.org/10.1016/j.scitotenv.2015.10.028. View in Article Google Scholar
[45]	Grell G. A., Peckham S. E., Schmitz R., et al. (2005). Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39:6957-6975. DOI:https://doi.org/10.1016/j.atmosenv.2005.04.027. View in Article Google Scholar
[46]	Ukhov A., Mostamandi S., da Silva A., et al. (2020). Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations. Atmos. Chem. Phys. 20:9281−9310. DOI:10.5194/acp-20-9281-2020 View in Article CrossRef Google Scholar
[47]	Li Q., Fu X., Peng X., et al. (2021). Halogens Enhance Haze Pollution in China. Environ. Sci. Technol. 55:13625−13637. DOI:10.1021/acs.est.1c01949 View in Article CrossRef Google Scholar
[48]	Gu B., Zhang L., Van Dingenen R., et al. (2021). Abating ammonia is more cost-effective than nitrogen oxides for mitigating PM2.5 air pollution. Science 374:758-762. DOI:10.1126/science.abf8623. View in Article Google Scholar
[49]	Hong C., Mueller N. D., Burney J. A., et al. (2020). Impacts of ozone and climate change on yields of perennial crops in California. Nat. Food 1:166−172. DOI:10.1038/s43016-020-0043-8 View in Article CrossRef Google Scholar
[50]	Liu Z., Zhou M., Chen Y., et al. (2021). The nonlinear response of fine particulate matter pollution to ammonia emission reductions in North China. Environ. Res. Lett. 16:034014. DOI:10.1088/1748-9326/abdf86 View in Article CrossRef Google Scholar
[51]	Danabasoglu G., Lamarque J. F., Bacmeister J., et al. (2020). The Community Earth System Model Version 2 (CESM2). J. Adv. Model. Earth Syst. 12:e2019MS001916. DOI:https://doi.org/10.1029/2019MS001916. View in Article Google Scholar
[52]	Zaveri R. A., Easter R. C., Fast J. D., et al. (2008). Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). J. Geophys. Res. Atmos. 113. DOI:https://doi.org/10.1029/2007JD008782. View in Article Google Scholar
[53]	Chen D., Liu Z., Fast J., et al. (2016). Simulations of sulfate–nitrate–ammonium (SNA) aerosols during the extreme haze events over northern China in October 2014. Atmos. Chem. Phys. 16:10707−10724. DOI:10.5194/acp-16-10707-2016 View in Article CrossRef Google Scholar
[54]	Guo Y., Chen Y., Searchinger T. D., et al. (2020). Air quality, nitrogen use efficiency and food security in China are improved by cost-effective agricultural nitrogen management. Nat. Food 1:648−658. DOI:10.1038/s43016-020-00162-z View in Article CrossRef Google Scholar
[55]	Zhong M., Saikawa E., Liu Y., et al. (2016). Air quality modeling with WRF-Chem v3.5 in East Asia: sensitivity to emissions and evaluation of simulated air quality. Geosci. Model Dev. 9:1201-1218. DOI:10.5194/gmd-9-1201-2016. View in Article Google Scholar
[56]	Liang X., Zhang S., Wu Y., et al. (2019). Air quality and health benefits from fleet electrification in China. Nat. Sustain. 2:962−971. DOI:10.1038/s41893-019-0398-8 View in Article CrossRef Google Scholar
[57]	Fu X., Cheng J., Peng L., et al. (2024). Co-benefits of transport demand reductions from compact urban development in Chinese cities. Nat. Sustain. 7:294−304. DOI:10.1038/s41893-024-01271-4 View in Article CrossRef Google Scholar
[58]	Zhou M., Liu H., Peng L., et al. (2022). Environmental benefits and household costs of clean heating options in northern China. Nat. Sustain. 5:329−338. DOI:10.1038/s41893-021-00837-w View in Article CrossRef Google Scholar
[59]	Organization W. H. (2021). WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. View in Article Google Scholar
[60]	Burnett R., Chen H., Szyszkowicz M., et al. (2018). Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc. Natl. Acad. Sci. U.S.A. 115:9592−9597. DOI:10.1073/pnas.1803222115 View in Article CrossRef Google Scholar
[61]	Peng L., Liu F., Zhou M., et al. (2021). Alternative-energy-vehicles deployment delivers climate, air quality, and health co-benefits when coupled with decarbonizing power generation in China. One Earth 4:1127−1140. DOI:10.1016/j.oneear.2021.07.007 View in Article CrossRef Google Scholar
[62]	He J. and Wang H. (2010). The value of statistical life: a contingent investigation in China. (The World Bank). View in Article Google Scholar
[63]	Lei Y. and Ho M. S. (2013). The valuation of health damages. In Clearer Skies over China: Reconciling Air Quality, Climate, and Economic Goals. (The MIT Press). View in Article Google Scholar
[64]	Xie X. (2011). The value of health: method for environmental impact assessment and strategis for urban air pollution control. DOI:https://doi.org/10.3390/ijerph18041559. View in Article Google Scholar
[65]	Li J., Liu H., Lv Z., et al. (2018). Estimation of PM2.5 mortality burden in China with new exposure estimation and local concentration-response function. Environ. Pollut. 243:1710-1718. DOI:https://doi.org/10.1016/j.envpol.2018.09.089. View in Article Google Scholar
[66]	Alsharef A., Banerjee S., Uddin S. M., et al. (2021). Early Impacts of the COVID-19 Pandemic on the United States Construction Industry. Int. J. Environ. Res. Public Health. View in Article Google Scholar
[67]	National Bureau of Statistics of China. (2016). China Statistical Yearbook 2016. https://www.stats.gov.cn/sj/ndsj/2016/indexeh.htm. View in Article Google Scholar
[68]	National Bureau of Statistics of China. (2019). China Statistical Yearbook 2019. https://www.stats.gov.cn/sj/ndsj/2019/indexeh.htm. View in Article Google Scholar
[69]	Li Y., Zhao X., Liao Q., et al. (2020). Specific differences and responses to reductions for premature mortality attributable to ambient PM2.5 in China. Sci. Total Environ. 742:140643. DOI:https://doi.org/10.1016/j.scitotenv.2020.140643. View in Article Google Scholar
[70]	McDuffie E. E., Martin R. V., Spadaro J. V., et al. (2021). Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales. Nat. Commun. 12:3594. DOI:10.1038/s41467-021-23853-y. View in Article Google Scholar
[71]	Ministry of Ecology and Environment of the People's Repulic of China. (2016). China Ecological Environment Statistical Yearbook 2016. https://www.mee.gov.cn/hjzl/sthjzk/sthjtjnb/202108/t20210827_860992.shtml. View in Article Google Scholar
[72]	Ministry of Ecology and Environment of the People's Repulic of China. (2021). China Ecological Environment Statistical Yearbook 2021. https://www.mee.gov.cn/hjzl/sthjzk/sthjtjnb/202301/t20230118_1013682.shtml. View in Article Google Scholar
[73]	Ministry of Ecology and Environment of the People's Repulic of China. (2017). China Ecological Environment Statistical Yearbook 2017. https://www.mee.gov.cn/hjzl/sthjzk/sthjtjnb/202108/t20210827_860994.shtml. View in Article Google Scholar
[74]	Ministry of Ecology and Environment of the People's Repulic of China. (2019). China Ecological Environment Statistical Yearbook 2019. https://www.mee.gov.cn/hjzl/sthjzk/sthjtjnb/202108/t20210827_861012.shtml. View in Article Google Scholar
[75]	Wang Y., Wen Y., Zhang S., et al. (2023). Vehicular Ammonia Emissions Significantly Contribute to Urban PM2.5 Pollution in Two Chinese Megacities. Environ. Sci. Technol. 57:2698-2705. DOI:10.1021/acs.est.2c06198. View in Article Google Scholar
[76]	Jiang Y., Xing J., Wang S., et al. (2020). Understand the local and regional contributions on air pollution from the view of human health impacts. Front. Environ. Sci. Eng. 15:88. DOI:10.1007/s11783-020-1382-2 View in Article CrossRef Google Scholar
[77]	Xing J., Ding D., Wang S., et al. (2019). Development and application of observable response indicators for design of an effective ozone and fine-particle pollution control strategy in China. Atmos. Chem. Phys. 19:13627−13646. DOI:10.5194/acp-19-13627-2019 View in Article CrossRef Google Scholar
[78]	Made-In-China (2024). Dust-proof net price. https://www.made-in-china.com/products-search/hot-china-products/Dust-Proof_Net.html. View in Article Google Scholar
[79]	National Bureau of Statistics of China. (2013). China Statistical Yearbook 2013. https://www.stats.gov.cn/sj/ndsj/2013/indexeh.htm. View in Article Google Scholar
[80]	National Bureau of Statistics of China. (2014). China Statistical Yearbook 2014. https://www.stats.gov.cn/sj/ndsj/2014/indexeh.htm. View in Article Google Scholar
[81]	National Bureau of Statistics of China. (2015). China Statistical Yearbook 2015. https://www.stats.gov.cn/sj/ndsj/2015/indexeh.htm. View in Article Google Scholar
[82]	National Bureau of Statistics of China. (2017). China Statistical Yearbook 2017. https://www.stats.gov.cn/sj/ndsj/2017/indexeh.htm. View in Article Google Scholar
[83]	Ministry of Ecology and Environment of the People's Repulic of China. (2013). 2013 China Environmental Status Bulletin. https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/201605/P020160526564151497131.pdf (accessed 27 May 2014). View in Article Google Scholar
[84]	Ministry of Ecology and Environment of the People's Repulic of China. (2017). 2017 China Environmental Status Bulletin. https://english.mee.gov.cn/Resources/Reports/soe/SOEE2017/201808/P020180801597738742758.pdf (accessed 22 May 2018). View in Article Google Scholar
[85]	Zhou W., Lei L., Du A., et al. (2022). Unexpected Increases of Severe Haze Pollution During the Post COVID-19 Period: Effects of Emissions, Meteorology, and Secondary Production. J. Geophys. Res. Atmos. 127:e2021JD035710. DOI:https://doi.org/10.1029/2021JD035710. View in Article Google Scholar
[86]	Xu B., Gu Z., Wang L., et al. (2019). Global Warming Increases the Incidence of Haze Days in China. J. Geophys. Res. Atmos. 124:6180-6190. DOI:https://doi.org/10.1029/2018JD030119. View in Article Google Scholar
[87]	Zhang Y., Yin Z. and Wang H. (2020). Roles of climate variability on the rapid increases of early winter haze pollution in North China after 2010. Atmos. Chem. Phys. 20:12211−12221. DOI:10.5194/acp-20-12211-2020 View in Article CrossRef Google Scholar
[88]	Yue H., He C., Huang Q., et al. (2020). Stronger policy required to substantially reduce deaths from PM2.5 pollution in China. Nat. Commun. 11:1462. DOI:10.1038/s41467-020-15319-4. View in Article Google Scholar
[89]	Lv Q., Yang Z., Chen Z., et al. (2024). Crop residue burning in China (2019–2021): Spatiotemporal patterns, environmental impact, and emission dynamics. Environ. Sci. Ecotechnol. 21:100394. DOI:https://doi.org/10.1016/j.ese.2024.100394. View in Article Google Scholar
[90]	Sharma G. K. and Ghuge V. V. (2024). How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities. Sci. Total Environ. 930:172399. DOI:https://doi.org/10.1016/j.scitotenv.2024.172399. View in Article Google Scholar

Cited by

ABOUT THIS ARTICLE

Cite this article:

Chen Z., Zhang C., Xu J., et al. (2025). The compulsory use of dust-proof nets led to significant environmental, health and economic benefits in China. The Innovation Geoscience 3:100160. https://doi.org/10.59717/j.xinn-geo.2025.100160

Chen Z., Zhang C., Xu J., et al. (2025). The compulsory use of dust-proof nets led to significant environmental, health and economic benefits in China. The Innovation Geoscience 3:100160. https://doi.org/10.59717/j.xinn-geo.2025.100160

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

Standard PDF

Extended PDF

Figures(7)

Article Metrics

Article views(3808) PDF downloads(1803)

The compulsory use of dust-proof nets led to significant environmental, health and economic benefits in China

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT

REFERENCES

ABOUT THIS ARTICLE

Cite this article:

Welcome!

Share

Article Metrics

Relative Articles

Cited by

Catalog

{{newsColumn.name}}

Official Account

{{newsColumn.name}}

Stay connected

{{newsColumn.name}}

The compulsory use of dust-proof nets led to significant environmental, health and economic benefits in China

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT

REFERENCES

ABOUT THIS ARTICLE

Cite this article:

Welcome!

Share

Article Metrics

Relative Articles

Cited by

Catalog

Export File

Citation

Format

Content

{{newsColumn.name}}

Official Account

{{newsColumn.name}}

Stay connected

{{newsColumn.name}}