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Cleanrooms often utilize large amounts of supply air to achieve a required cleanliness level. To reduce the overall supply air volume, critical processes demanding the highest cleanliness requirements are suggested to be placed directly beneath the air outlet of fan filter units (FFUs). In order to determine an appropriate supply air volume, it is necessary to quantitatively analyze the particulate concentration distribution downstream of FFUs with various characteristics to determine an adequate but not excessive supply air volume. Three FFU sizes and four supply air velocities were used in this experiment, and the resulting airstream velocities, jet diffusion widths, and characteristics of particulate concentration distribution were obtained. Fitting expressions were statistically acquired based on the respective experimental data sets, which can be used to predict the airflow velocity value at any point in an FFU flow region and the width of the air cleanliness protection range.

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standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens.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.

Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy 1 , 2 , have led to public health benefits 1 – 4 . However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM 2.5 ), a pollutant regulated under the Clean Air Act 1 , have stagnated or begun to reverse throughout much of the USA 5 . Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM 2.5 trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM 2.5 concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM 2.5 concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM 2.5 concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM 2.5 concentrations are unregulated under current air pollution law 6 and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm. Ground- and satellite-based air pollution data from 2000 to 2022 quantify the contribution of wildfire smoke to stagnation or reversal in PM 2.5 concentration trends, showing that this contribution will grow as the climate continues to warm.

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. China’s clean air action stimulated a net accumulative reduction of 2.43 Gt CO 2 emission from 2013-2020. Phase-out and upgrades of outdated, polluting, and inefficient combustion facilities have promoted the transition of the country’s energy system.

Beginning in 2013, China launched two phases (2013–2017 and 2018–2020) of clean air actions that have led to substantial reductions in PM 2.5 concentrations. However, improvement in PM 2.5 pollution was notably slowing down during Phase II. Here we quantify the efficacy and drivers of PM 2.5 improvement and evaluate the associated cost during 2013–2020 using an integrated framework that combines an emission inventory model, a chemical transport model and detailed cost information. We found that national population-weighted mean PM 2.5 concentrations decreased by 19.8 μg m −3 and 10.9 μg m −3 in the two phases, and the contribution of clean air policies in Phase II (2.3 μg m −3  yr −1 ) was considerably lower than that of Phase I (4.5 μg m −3  yr −1 ), after excluding the impacts from meteorological condition changes and COVID-19 lockdowns. Enhanced structure transitions and targeted volatile organic compounds and NH 3 reduction measures have successfully reduced emissions in Phase II, but measures focusing on the end-of-pipe control were less effective after 2017. From 2013 to 2020, PM 2.5 abatement became increasingly challenging, with the average cost of reducing one unit of PM 2.5 concentration in Phase II twice that of Phase I. Our results suggest there is a need for strengthened, well-balanced, emission control strategies for multi-pollutants. China’s second phase of clean air actions proved less effective than the first, highlighting the need to adapt and update policies to enable continued progress, according to an assessment combining chemical transport modelling and emission inventories.

Surface ozone data from the Chinese Ministry of Ecology and Environment (MEE) network show sustained increases across the country over the 2013–2019 period. Despite Phase 2 of the Clean Air Action Plan targeting ozone pollution, ozone was higher in 2018–2019 than in previous years. The mean summer 2013–2019 trend in maximum 8 h average (MDA8) ozone was 1.9 ppb a−1 (p<0.01) across China and 3.3 ppb a−1 (p<0.01) over the North China Plain (NCP). Fitting ozone to meteorological variables with a multiple linear regression model shows that meteorology played a significant but not dominant role in the 2013–2019 ozone trend, contributing 0.70 ppb a−1 (p<0.01) across China and 1.4 ppb a−1 (p=0.02) over the NCP. Rising June–July temperatures over the NCP were the main meteorological driver, particularly in recent years (2017–2019), and were associated with increased foehn winds. NCP data for 2017–2019 show a 15 % decrease in fine particulate matter (PM2.5) that may be driving the continued anthropogenic increase in ozone, as well as unmitigated emissions of volatile organic compounds (VOCs). VOC emission reductions, as targeted by Phase 2 of the Chinese Clean Air Action Plan, are needed to reverse the increase in ozone.

ObjectiveCigarette smoking is a major risk factor for colorectal cancer (CRC). We aimed to investigate whether cigarette smoke promotes CRC by altering the gut microbiota and related metabolites.DesignAzoxymethane-treated C57BL/6 mice were exposed to cigarette smoke or clean air 2 hours per day for 28 weeks. Shotgun metagenomic sequencing and liquid chromatography mass spectrometry were parallelly performed on mice stools to investigate alterations in microbiota and metabolites. Germ-free mice were transplanted with stools from smoke-exposed and smoke-free control mice.ResultsMice exposed to cigarette smoke had significantly increased tumour incidence and cellular proliferation compared with smoke-free control mice. Gut microbial dysbiosis was observed in smoke-exposed mice with significant differential abundance of bacterial species including the enrichment of Eggerthella lenta and depletion of Parabacteroides distasonis and Lactobacillus spp. Metabolomic analysis showed increased bile acid metabolites, especially taurodeoxycholic acid (TDCA) in the colon of smoke-exposed mice. We found that E. lenta had the most positive correlation with TDCA in smoke-exposed mice. Moreover, smoke-exposed mice manifested enhanced oncogenic MAPK/ERK (mitogen-activated protein kinase/extracellular signal‑regulated protein kinase 1/2) signalling (a downstream target of TDCA) and impaired gut barrier function. Furthermore, germ-free mice transplanted with stools from smoke-exposed mice (GF-AOMS) had increased colonocyte proliferation. Similarly, GF-AOMS showed increased abundances of gut E. lenta and TDCA, activated MAPK/ERK pathway and impaired gut barrier in colonic epithelium.ConclusionThe gut microbiota dysbiosis induced by cigarette smoke plays a protumourigenic role in CRC. The smoke-induced gut microbiota dysbiosis altered gut metabolites and impaired gut barrier function, which could activate oncogenic MAPK/ERK signalling in colonic epithelium.

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.

The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM2.5: a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM2.5 and gas precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed -3.9 % to 2.8 % of the annual mean PM2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., -6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, -5.1 % to 4.3 % in the Fenwei Plain,-4.8 % to 4.3 % in the Yangtze River Delta, and -25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.