Mental disorders have been associated with various aspects of anthropogenic change to the environment, but the relative effects of different drivers are uncertain. Here we estimate associations ...between multiple environmental factors (air quality, residential greenness, mean temperature, and temperature variability) and self-assessed mental health scores for over 20,000 Chinese residents. Mental health scores were surveyed in 2010 and 2014, allowing us to link changes in mental health to the changes in environmental variables. Increases in air pollution and temperature variability are associated with higher probabilities of declined mental health. Mental health is statistically unrelated to mean temperature in this study, and the effect of greenness on mental health depends on model settings, suggesting a need for further study. Our findings suggest that the environmental policies to reduce emissions of air pollution or greenhouse gases can improve mental health of the public in China.
Ambient exposure to fine particulate matter (PM2.5) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with ...in-situ observations after 2013 to predict PM2.5 concentrations nationwide, while the lack of surface monitoring data before 2013 have created difficulties in historical PM2.5 exposure estimates. Hindcast approaches using statistical models or chemical transport models (CTMs) were developed to overcome this limitation, while those approaches still suffer from incomplete daily coverage due to missing AOD data or limited accuracy due to uncertainties of CTMs. Here we developed a new machine learning (ML) model with high-dimensional expansion (HD-expansion) of numerous predictors (including AOD and other satellite covariates, meteorological variables and CTM simulations). Through comprehensive characterization of the nonlinear effects of, and interactions among different predictors, the HD-expansion parameterized the association between PM2.5 and AOD as a nonlinear function of space and time covariates (e.g., planetary boundary layer height and relative humidity). In this way, the PM2.5-AOD association can vary spatiotemporally. We trained the model with data from 2013 to 2016 and evaluated its performance using annually-iterated cross-validation, which iteratively held out the in-situ observations for a whole calendar year (as testing data) to examine the predictions from a model trained by the rest of the observations. Our estimates were found to be in good agreement with in-situ observations, with correlation coefficients (R2) of 0.61, 0.68, and 0.75 for daily, monthly and annual averages, respectively. To interpolate the missing predictions due to incomplete AOD data, we incorporated a generalized additive model into the ML model. The two-stage estimates of PM2.5 sacrificed the prediction accuracy on a daily timescale (R2 = 0.55), but achieved complete spatiotemporal coverage and improved the accuracy of monthly (R2 = 0.71) and annual (R2 = 0.77) averages. The model was then used to predict daily PM2.5 concentrations during 2000–2016 across China and estimate long-term trends in PM2.5 for the period. We found that population-weighted concentrations of PM2.5 significantly increased, by 2.10 (95% confidence interval (CI): 1.74, 2.46) μg/m3/year during 2000–2007, and rapidly decreased by 4.51 (3.12, 5.90) μg/m3/year during 2013–2016. In this study, we produced AOD-based estimates of historical PM2.5 with complete spatiotemporal coverage, which were evidenced as accurate, particularly in middle and long term. The products could support large-scale epidemiological studies and risk assessments of ambient PM2.5 in China and can be accessed via the website (http://www.meicmodel.org/dataset-phd.html).
Display omitted
•Estimating historical PM2.5 in China is difficult due to lack of in-situ data.•Hindcast approaches suffers from incomplete daily coverage due to missing AOD.•We design a model with high-dimensional representation of many predictors of PM2.5.•We generate daily PM2.5 maps with a spatial resolution of 0.1° in China, 2000–2016.•The spatiotemporally continuous estimates are well correlated with in-situ PM2.5.
Net anthropogenic emissions of carbon dioxide (CO
) must approach zero by mid-century (2050) in order to stabilize the global mean temperature at the level targeted by international efforts
. Yet ...continued expansion of fossil-fuel-burning energy infrastructure implies already 'committed' future CO
emissions
. Here we use detailed datasets of existing fossil-fuel energy infrastructure in 2018 to estimate regional and sectoral patterns of committed CO
emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of the associated infrastructure. We estimate that, if operated as historically, existing infrastructure will cumulatively emit about 658 gigatonnes of CO
(with a range of 226 to 1,479 gigatonnes CO
, depending on the lifetimes and utilization rates assumed). More than half of these emissions are predicted to come from the electricity sector; infrastructure in China, the USA and the 28 member states of the European Union represents approximately 41 per cent, 9 per cent and 7 per cent of the total, respectively. If built, proposed power plants (planned, permitted or under construction) would emit roughly an extra 188 (range 37-427) gigatonnes CO
. Committed emissions from existing and proposed energy infrastructure (about 846 gigatonnes CO
) thus represent more than the entire carbon budget that remains if mean warming is to be limited to 1.5 degrees Celsius (°C) with a probability of 66 to 50 per cent (420-580 gigatonnes CO
)
, and perhaps two-thirds of the remaining carbon budget if mean warming is to be limited to less than 2 °C (1,170-1,500 gigatonnes CO
)
. The remaining carbon budget estimates are varied and nuanced
, and depend on the climate target and the availability of large-scale negative emissions
. Nevertheless, our estimates suggest that little or no new CO
-emitting infrastructure can be commissioned, and that existing infrastructure may need to be retired early (or be retrofitted with carbon capture and storage technology) in order to meet the Paris Agreement climate goals
. Given the asset value per tonne of committed emissions, we suggest that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternatives are available and affordable
.
Achieving carbon neutrality before 2060 newly announced in China are expected to substantially affect air quality. Here we project the pollutants emissions in China based on a carbon neutrality ...roadmap and clean air policies evolution; national and regional PM2.5 and O3 concentrations in 2030 (the target year of carbon peak), 2035 (the target year of “Beautiful China 2035” launched by the Chinese government to fundamentally improve air quality) and 2060 (the target year of carbon neutrality) are then simulated using an air quality model. Results showed that compared with 2019, emissions of SO2, NOx, primary PM2.5, and VOCs are projected to reduce by 42%, 42%, 44%, and 28% in 2030, by 57%, 58%, 60%, and 42% in 2035, by 93%, 93%, 90% and 61% in 2060 respectively. Consequently, in 2030, 2035, and 2060, the national annual mean PM2.5 will be 27, 23, and 11 μg m−3; and the 90th percentile of daily 8-h maxima of O3 (O3-8h 90th) will be 129, 123, and 93 μg m−3; 82%, 94%, and 100% of 337 municipal cities will reach the current national air quality standard, respectively. It's expected that the “Beautiful China 2035” target is very likely to be achieved, and about half of the 337 cities will meet the current WHO air quality guideline in 2060. In the near future, strict environmental policies driven by “Beautiful China 2035” are needed due to their substantial contribution to emission reductions. By 2060, the low-carbon policies driven by the carbon neutrality target are expected to contribute to larger than 80% of reductions in PM2.5 and O3-8h 90th concentrations relative to the 2020 levels, implying that more attention could be paid to low-carbon policies after 2035. Our research would provide implications for future co-governance of air pollution and climate change mitigation in China and other developing countries.
Display omitted
•The air quality cobenefits of achieving China's carbon neutrality before 2060 was quantitatively investigated.•This study proposed a mid-to-long-term air quality improvement pathway until 2060 of China (CAEP-CAP).•End-of-pipe controls can greatly improve air quality before 2035, while low-carbon policy will be more critical after 2035.
Numerous previous studies have revealed that statistical models which combine satellite-derived aerosol optical depth (AOD) and PM2.5 measurements acquired at scattered monitoring sites provide an ...effective method for deriving continuous spatial distributions of ground-level PM2.5 concentrations. Using the national monitoring networks that have recently been established by central and local governments in China, we developed linear mixed-effects (LMEs) models that integrate Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements, meteorological parameters, and satellite-derived tropospheric NO2 column density measurements as predictors to estimate PM2.5 concentrations over three major industrialized regions in China, namely, the Beijing–Tianjin–Hebei region (BTH), the Yangtze River Delta region (YRD), and the Pearl River Delta region (PRD). The models developed for these three regions exploited different predictors to account for their varying topographies and meteorological conditions. Considering the importance of unbiased PM2.5 predictions for epidemiological studies, the correction factors calculated from the surface PM2.5 measurements were applied to correct biases in the predicted annual average PM2.5 concentrations introduced by non-stochastic missing AOD measurements. Leave-one-out cross-validation (LOOCV) was used to quantify the accuracy of our models. Cross-validation of the daily predictions yielded R2 values of 0.77, 0.8 and 0.8 and normalized mean error (NME) values of 22.4%, 17.8% and 15.2% for BTH, YRD and PRD, respectively. For the annual average PM2.5 concentrations, the LOOCV R2 values were 0.85, 0.76 and 0.71 for the three regions, respectively, whereas the LOOCV NME values were 8.0%, 6.9% and 8.4%, respectively. We found that the incorporation of satellite-based NO2 column density into the LMEs model contribute to considerable improvements in annual prediction accuracy for both BTH and YRD. The satisfactory performance of our models indicates that constructing LMEs models using various combinations of predictors for different regions would be helpful for predicting PM2.5 concentrations with high accuracy.
•We assessed the ground-level PM2.5 concentrations over BTH, YRD, and PRD in China.•Feasibility of LMEs models in different regions was confirmed.•AOD sampling biases were accounted by PM2.5 measurement-derived correction factor.•Satellite-based NO2 column improved annual prediction accuracy in BTH and YRD.
Air pollution has altered the Earth’s radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with ...timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R 2 of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM2.5 data allow us to track the day-to-day variations in PM2.5 concentrations over China in a timely manner. The long-term records of PM2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM2.5 data are publicly available through our website for sharing with the research and policy communities.
PM2.5 chemical components play significant roles in the climate, air quality, and public health, and the roles vary due to their different physicochemical properties. Obtaining accurate and timely ...updated information on China’s PM2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM2.5 chemical composition data set at 10 km spatial resolution since 2000, combining the Weather Research and Forecasting–Community Multiscale Air Quality modeling system, ground observations, a machine learning algorithm, and multisource-fusion PM2.5 data. PM2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at a monthly scale from 2000 to 2020 and from 0.67 to 0.80 at a daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM2.5 chemical composition in China, especially the rapid decreases after 2013 for sulfate, nitrate, ammonium, organic matter, and black carbon, at the rate of −9.0, −7.2, −8.1, −8.4, and −9.2% per year, respectively. The day-to-day variability is also well captured, including evolutions in spatial distribution and shares of PM2.5 components. As part of Tracking Air Pollution in China (http://tapdata.org.cn), this daily-updated data set provides large opportunities for health and climate research as well as policy-making in China.
To tackle the problem of severe air pollution, China has implemented active
clean air policies in recent years. As a consequence, the emissions of major
air pollutants have decreased and the air ...quality has substantially improved.
Here, we quantified China's anthropogenic emission trends from 2010 to 2017
and identified the major driving forces of these trends by using a
combination of bottom-up emission inventory and index decomposition analysis
(IDA) approaches. The relative change rates of China's anthropogenic
emissions during 2010–2017 are estimated as follows: −62 % for
SO2, −17 % for NOx, +11 % for nonmethane
volatile organic compounds (NMVOCs), +1 % for NH3, −27 %
for CO, −38 % for PM10, −35 % for PM2.5, −27 %
for BC, −35 % for OC, and +16 % for CO2. The IDA results
suggest that emission control measures are the main drivers of this
reduction, in which the pollution controls on power plants and industries are
the most effective mitigation measures. The emission reduction rates markedly
accelerated after the year 2013, confirming the effectiveness of China's
Clean Air Action that was implemented since 2013. We estimated that during
2013–2017, China's anthropogenic emissions decreased by 59 % for
SO2, 21 % for NOx, 23 % for CO, 36 % for
PM10, 33 % for PM2.5, 28 % for BC, and 32 % for OC.
NMVOC emissions increased and NH3 emissions remained stable during
2010–2017, representing the absence of effective mitigation measures for
NMVOCs and NH3 in current policies. The relative contributions of
different sectors to emissions have significantly changed after several
years' implementation of clean air policies, indicating that it is paramount
to introduce new policies to enable further emission reductions in the
future.
Aggressive emission control measures were taken by the Chinese government after the promulgation of the 'Air Pollution Prevention and Control Action Plan' in 2013. Here we evaluated the air quality ...and health benefits associated with this stringent policy during 2013-2015 by using surface PM2.5 concentrations estimated from a three-stage data fusion model and cause-specific integrated exposure-response functions. The population-weighted annual mean PM2.5 concentrations decreased by 21.5% over China during 2013-2015, reducing from 60.5 in 2013 to 47.5 μg m−3 in 2015. Subsequently, the national PM2.5-attributable mortality decreased from 1.22 million (95% CI: 1.05, 1.37) in 2013 to 1.10 million (95% CI: 0.95, 1.25) in 2015, which is a 9.1% reduction. The limited health benefits compared to air quality improvements are mainly due to the supralinear responses of mortality to PM2.5 over the high concentration end of the concentration-response functions. Our study affirms the effectiveness of China's recent air quality policy; however, due to the nonlinear responses of mortality to PM2.5 variations, current policies should remain in place and more stringent measures should be implemented to protect public health.
Abstract
Climate change mitigation measures can yield substantial air quality improvements while emerging clean air measures in developing countries can also lead to CO
2
emission mitigation ...co-benefits by affecting the local energy system. Here, we evaluate the effect of China’s stringent clean air actions on its energy use and CO
2
emissions from 2013-2020. We find that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean air actions have promoted the transformation of the country’s energy system. The co-benefits of China’s clean air measures far outweigh the additional CO
2
emissions of end-of-pipe devices, realizing a net accumulative reduction of 2.43 Gt CO
2
from 2013-2020, exceeding the accumulated CO
2
emission increase in China (2.03 Gt CO
2
) during the same period. Our study indicates that China’s efforts to tackle air pollution induce considerable climate benefit, and measures with remarkable CO
2
reduction co-benefits deserve further attention in future policy design.
PDF
