From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

https://www.pnas.org/content/pnas/116/49/24463.full.pdf

Drivers of improved PM2.5 air quality in China from 2013 to 2017.

Observations of surface ozone available from ∼1,000 sites across China for the past 5 years (2013–2017) show severe summertime pollution and regionally variable trends. We resolve the effect of meteorological variability on the ozone trends by using a multiple linear regression model. The residual of this regression shows increasing ozone trends of 1–3 ppbv a−1 in megacity clusters of eastern China that we attribute to changes in anthropogenic emissions. By contrast, ozone decreased in some areas of southern China. Anthropogenic NOx emissions in China are estimated to have decreased by 21% during 2013–2017, whereas volatile organic compounds (VOCs) emissions changed little. Decreasing NOx would increase ozone under the VOC-limited conditions thought to prevail in urban China while decreasing ozone under rural NOx-limited conditions. However, simulations with the Goddard Earth Observing System Chemical Transport Model (GEOS-Chem) indicate that a more important factor for ozone trends in the North China Plain is the ∼40% decrease of fine particulate matter (PM2.5) over the 2013–2017 period, slowing down the aerosol sink of hydroperoxy (HO2) radicals and thus stimulating ozone production.

https://www.pnas.org/content/pnas/116/2/422.full.pdf

Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China

This study investigates the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009. Two complementary statistical techniques were adopted to evaluate the possible nonstationary behavior of these precipitation data. The first was a Mann‐Kendall nonparametric trend test, and it was used to evaluate the existence of monotonic trends. The second was a nonstationary generalized extreme value analysis, and it was used to determine the strength of association between the precipitation extremes and globally averaged near-surface temperature. The outcomes are that statistically significant increasing trends can be detected at the global scale, with close to two-thirds of stations showing increases. Furthermore, there is a statistically significant association with globally averaged near-surface temperature,withthemedianintensityofextremeprecipitationchanginginproportionwithchangesinglobal mean temperature at a rate of between 5.9% and 7.7%K 21 , depending on the method of analysis. This ratio was robust irrespective of record length or time period considered and was not strongly biased by the uneven global coverage of precipitation data. Finally, there is a distinct meridional variation, with the greatest sensitivity occurring in the tropics and higher latitudes and the minima around 138S and 118N. The greatest uncertainty was near the equator because of the limited number of sufficiently long precipitation records, and there remains an urgent need to improve data collection in this region to better constrain future changes in tropical precipitation.

Global increasing trends in annual maximum daily precipitation

The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

https://www.mdpi.com/2072-4292/11/1/48/pdf

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Abstract. Fine particulate matter (PM2.5) is a severe air pollution\nproblem in China. Observations of PM2.5 have been available since 2013\nfrom a large network operated by the China National Environmental Monitoring\nCenter (CNEMC). The data show a general 30 %–50 % decrease in annual mean\nPM2.5 across China over the 2013–2018 period, averaging at −5.2 µg m−3 a−1. Trends in the five megacity cluster regions targeted by\nthe government for air quality control are -9.3±1.8 µg m−3 a−1 (±95 % confidence interval) for Beijing–Tianjin–Hebei,\n-6.1±1.1 µg m−3 a−1 for the Yangtze River Delta, -2.7±0.8 µg m−3 a−1 for the Pearl River Delta, -6.7±1.3 µg m−3 a−1 for the Sichuan Basin, and -6.5±2.5 µg m−3 a−1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018\nobservations of sulfur dioxide (SO2) and carbon monoxide (CO) show that\nthe declines in PM2.5 are qualitatively consistent with drastic\ncontrols of emissions from coal combustion. However, there is also a large\nmeteorologically driven interannual variability in PM2.5 that\ncomplicates trend attribution. We used a stepwise multiple linear regression\n(MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5\nanomalies to wind speed, precipitation, relative humidity, temperature, and\n850 hPa meridional wind velocity (V850). The meteorology-corrected\nPM2.5 trends after removal of the MLR meteorological contribution can\nbe viewed as being driven by trends in anthropogenic emissions. The mean\nPM2.5 decrease across China is −4.6 µg m−3 a−1 in the\nmeteorology-corrected data, 12 % weaker than in the original data, meaning\nthat 12 % of the PM2.5 decrease in the original data is\nattributable to meteorology. The trends in the meteorology-corrected data\nfor the five megacity clusters are -8.0±1.1 µg m−3 a−1 for Beijing–Tianjin–Hebei (14 % weaker than in the original\ndata), -6.3±0.9 µg m−3 a−1 for the Yangtze River Delta\n(3 % stronger), -2.2±0.5 µg m−3 a−1 for the Pearl River\nDelta (19 % weaker), -4.9±0.9 µg m−3 a−1 for\nthe Sichuan Basin (27 % weaker), and -5.0±1.9 µg m−3 a−1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of\nflattening PM2.5 in the Pearl River Delta and increases in the Fenwei\nPlain can be attributed to meteorology rather than to relaxation of emission\ncontrols.\n

/pdf/fine-particulate-matter-pm-2-5-trends-in-china-2013-2018-4nxiozdatx.pdf

Fine particulate matter (PM 2.5 ) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

The recent severe and frequent PM2.5 (i.e., fine particles smaller than 2.5 µm) pollution in China has aroused unprecedented public concern. The first two years of PM2.5 measurements in Chi...

PM2.5 Pollution in China and How It Has Been Exacerbated by Terrain and Meteorological Conditions

This study compared the diurnal and seasonal cycles of atmospheric and surface urban heat islands (UHIs) based on hourly air temperatures (Ta) collected at 65 out of 262 stations in Beijing and land surface temperature (Ts) derived from Moderate Resolution Imaging Spectroradiometer in the years 2013–2014. We found that the nighttime atmospheric and surface UHIs referenced to rural cropland stations exhibited significant seasonal cycles, with the highest in winter. However, the seasonal variations in the nighttime UHIs referenced to mountainous forest stations were negligible, because mountainous forests have a higher nighttime Ts in winter and a lower nighttime Ta in summer than rural croplands. Daytime surface UHIs showed strong seasonal cycles, with the highest in summer. The daytime atmospheric UHIs exhibited a similar but less seasonal cycle under clear-sky conditions, which was not apparent under cloudy-sky conditions. Atmospheric UHIs in urban parks were higher in daytime. Nighttime atmospheric UHIs are influenced by energy stored in urban materials during daytime and released during nighttime. The stronger anthropogenic heat release in winter causes atmospheric UHIs to increase with time during winter nights, but decrease with time during summer nights. The percentage of impervious surfaces is responsible for 49%–54% of the nighttime atmospheric UHI variability and 31%–38% of the daytime surface UHI variability. However, the nighttime surface UHI was nearly uncorrelated with the percentage of impervious surfaces around the urban stations.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD025304

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018GL077424

A Revisit of Global Dimming and Brightening Based on the Sunshine Duration

Land surface temperature Ts provides essential supplementary information to surface air temperature, the most widely used metric in global warming studies. A lack of reliable observational Ts data makes assessing model simulations difficult. Here, the authors first examined the simulated Ts of eight current reanalyses based on homogenized Ts data collected at ~2200 weather stations from 1979 to 2003 in China. The results show that the reanalyses are skillful in simulating the interannual variance of Ts in China (r = 0.95) except over the Tibetan Plateau. ERA-Interim and MERRA land versions perform better in this respect than ERA-Interim and MERRA. Observations show that the interannual variance of Ts over the north China plain and south China is mostly influenced by surface incident solar radiation Rs, followed by precipitation frequency, whereas the opposite is true over the northwest China, northeast China, and the Tibetan Plateau. This variable relationship is well captured by ERA-Interim, ERA-...

Evaluation of Eight Current Reanalyses in Simulating Land Surface Temperature from 1979 to 2003 in China

Land surface air temperature (LSAT) has been a widely used metric to study climate change. Weather observations of LSAT are the fundamental data for climate change studies and provide key evidence of global warming. However, there are very few meteorological observations over deserts due to their uninhabitable environment. This study fills this gap and provides independent evidence using satellite-derived land surface temperatures (LSTs), benefiting from their global coverage. The frequency of clear sky from MODerate Resolution Imaging Spectroradiometer (MODIS) LST data over global deserts was found to be greater than 94% for the 2002–2015 period. Our results show that MODIS LST has a bias of 1.36°C compared to ground-based observations collected at 31 U.S. Climate Reference Network (USCRN) stations, with a standard deviation of 1.83°C. After bias correction, MODIS LST was used to evaluate existing reanalyses, including ERA-Interim, Japanese 55-year Reanalysis (JRA-55), Modern-Era Retrospective Analysis for Research and Applications (MERRA), MERRA-land, National Centers for Environmental Prediction (NCEP)-R1, and NCEP-R2. The reanalyses accurately reproduce the seasonal cycle and interannual variability of the LSTs, but their multiyear means and trends of LSTs exhibit large uncertainties. The multiyear averaged LST over global deserts is 23.5°C from MODIS and varies from 20.8°C to 24.5°C in different reanalyses. The MODIS LST over global deserts increased by 0.25°C/decade from 2002 to 2015, whereas the reanalyses estimated a trend varying from −0.14 to 0.10°C/decade. The underestimation of the LST trend by the reanalyses occurs for approximately 70% of the global deserts, likely due to the imperfect performance of the reanalyses in reproducing natural climate variability.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD025410

Chunlüe Zhou

Papers

PM2.5 Pollution in China and How It Has Been Exacerbated by Terrain and Meteorological Conditions

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

A Revisit of Global Dimming and Brightening Based on the Sunshine Duration

Evaluation of Eight Current Reanalyses in Simulating Land Surface Temperature from 1979 to 2003 in China

Land surface temperature over global deserts: Means, variability, and trends