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The seasonality of vegetation, i.e., developmental stages and phenological processes, affects the emission of volatile organic compounds (VOCs). Despite the potential significance, the contributions of seasonality to VOC emission quality and quantity are not well understood and are therefore often ignored in emission simulations. We investigated the VOC emission patterns of young and mature leaves of several Mediterranean plant species in relation to their physiological and developmental changes during the growing period and estimated Es. Foliar emissions of isoprenoids and oxygenated VOCs like methanol and acetone were measured online by means of a proton transfer reaction mass spectrometer (PTR‐MS) and offline with gas chromatography coupled with a mass spectrometer and flame ionization detector. The results suggest that VOC emission is a developmentally regulated process and that quantitative and qualitative variability is plant species specific. Leaf ontogeny clearly influenced both the VOC Es and the relative importance of different VOCs. Methanol was the major compound contributing to the sum of target VOC emissions in young leaves (11.8 ± 10.4 μg g−1 h−1), while its contribution was minor in mature leaves (4.1 ± 4.1 μg g−1 h−1). Several plant species showed a decrease or complete subsidence of monoterpene, sesquiterpene, and acetone emissions upon maturity, perhaps indicating a potential response to the higher defense demands of young emerging leaves. Key Points Developmental stage and phenological processes affect VOC emission Methanol emissions from young leaves were higher than from mature leaves Plants showed decrease or subsidence of monoterpene, sesquiterpene upon maturity

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.

The EDGARv4.3.1 (Emissions Database for Global Atmospheric Research) global anthropogenic emissions inventory of gaseous (SO2, NOx, CO, non-methane volatile organic compounds and NH3) and particulate (PM10, PM2.5, black and organic carbon) air pollutants for the period 1970–2010 is used to develop retrospective air pollution emissions scenarios to quantify the roles and contributions of changes in energy consumption and efficiency, technology progress and end-of-pipe emission reduction measures and their resulting impact on health and crop yields at European and global scale. The reference EDGARv4.3.1 emissions include observed and reported changes in activity data, fuel consumption and air pollution abatement technologies over the past 4 decades, combined with Tier 1 and region-specific Tier 2 emission factors. Two further retrospective scenarios assess the interplay of policy and industry. The highest emission STAG_TECH scenario assesses the impact of the technology and end-of-pipe reduction measures in the European Union, by considering historical fuel consumption, along with a stagnation of technology with constant emission factors since 1970, and assuming no further abatement measures and improvement imposed by European emission standards. The lowest emission STAG_ENERGY scenario evaluates the impact of increased fuel consumption by considering unchanged energy consumption since the year 1970, but assuming the technological development, end-of-pipe reductions, fuel mix and energy efficiency of 2010. Our scenario analysis focuses on the three most important and most regulated sectors (power generation, manufacturing industry and road transport), which are subject to multi-pollutant European Union Air Quality regulations. Stagnation of technology and air pollution reduction measures at 1970 levels would have led to 129 % (or factor 2.3) higher SO2, 71 % higher NOx and 69 % higher PM2.5 emissions in Europe (EU27), demonstrating the large role that technology has played in reducing emissions in 2010. However, stagnation of energy consumption at 1970 levels, but with 2010 fuel mix and energy efficiency, and assuming current (year 2010) technology and emission control standards, would have lowered today's NOx emissions by ca. 38 %, SO2 by 50 % and PM2.5 by 12 % in Europe. A reduced-form chemical transport model is applied to calculate regional and global levels of aerosol and ozone concentrations and to assess the associated impact of air quality improvements on human health and crop yield loss, showing substantial impacts of EU technologies and standards inside as well as outside Europe. We assess that the interplay of policy and technological advance in Europe had substantial benefits in Europe, but also led to an important improvement of particulate matter air quality in other parts of the world.

BackgroundRoad transport is an important contributor to the European Union’s total greenhouse gas emissions. This study aims at summarizing methane (CH4) and nitrous oxide (N2O) exhaust emissions from L-category, light-duty and heavy-duty vehicles in the European Union. The assessment is based on measurements carried out in the Vehicle Emission Laboratory of the Joint Research Centre between 2009 and 2019. The exhaust chemical composition from a fleet of 38 L-category vehicles Euro 1 to Euro 4 (2- and 3-wheelers, small quadricycles such as quads and minicars), 63 light-duty vehicles from Euro 5b to Euro 6d-TEMP (passenger cars, including hybrid vehicles), and 27 light commercial and heavy-duty vehicles from pre-Euro I to Euro VI (including lorries, buses and garbage trucks) was analyzed by Fourier-transform infrared spectroscopy.ResultsCH4 emission factors monitored were from 1 to 234 mg/km for L-category vehicles (mean: 39 mg/km), from 0.1 to 40 mg/km for light-duty vehicles (mean: 7 mg/km), and from non-detectable to 320 mg/km for heavy-duty vehicles (mean: 19 mg/km). N2O emission factors monitored were from non-detectable to 5 mg/km for L-category vehicles (mean: 1 mg/km), from non-detectable to 40 mg/km for light-duty vehicles (mean: 7 mg/km), and from non-detectable to 118 mg/km for heavy-duty vehicles (mean: 19 mg/km). According to the 100-year Global Warming Potential of these greenhouse gases, these emissions corresponded to a range from negligible up to 9 g/km of CO2-equivalent for CH4 and from negligible up to 32 g/km of CO2-equivalent for N2O.ConclusionsThe higher contributors of CH4 were the two-stroke mopeds included in the L-category vehicles, while the higher emissions of N2O were found in the modern (Euro 5–6 or Euro V–VI) diesel light- and heavy-duty vehicles. Among them, vehicles complying with Euro 6 and Euro VI standard were associated to higher N2O emissions compared to those associated to Euro 5 and pre-Euro IV standards, which could be attributed to the introduction of the after-treatment systems designed to fulfill more stringent NOx standards. These updated emission factors and unique on its kind database represent a source of information for legislators and modelers to better assess the greenhouse gas emission reduction in the EU transport sector.

Although air quality guidelines generally use the atmospheric concentration of fine particulate matter (PM2.5) as a metric for air pollution evaluation and management, the fact cannot be ignored that different particle toxicities are unequal and significantly related to their sources and chemical compositions. Therefore, judging the most harmful source and identifying the toxic component would be helpful for optimizing air quality standards and prioritizing targeted PM2.5 control strategies to protect public health more effectively. Since the combustions of fuels, including oil, coal, and biomass, are the main anthropogenic sources of environmental PM2.5, their discrepant contributions to health risks of mixed ambient aerosol pollution dominated by the respective emission intensity and unequal toxicity of chemical components need to be identified. In order to quantify the differences between these combustion primary emissions, 10 types of PM2.5 from each typical source group, i.e., vehicle exhaust, coal combustion, and plant biomass (domestic biofuel) burning, were collected for comparative study with toxicological mechanisms. In total, 30 types of individual combustion samples were intercompared with representative urban ambient air PM2.5 samples, whose chemical characteristics and biological effects were investigated by component analysis (carbon, metals, soluble ions) and in vitro toxicity assays (cell viability, oxidative stress, inflammatory response) of human lung adenocarcinoma epithelial cells (A549). Carbonaceous fractions were plenteous in automobile exhaust and biomass burning, while heavy metals were more plentiful in PM2.5 from coal combustion and automobile exhaust. The overall ranking of mass-normalized cytotoxicity for source-specific PM2.5 was automobile exhaust > coal combustion > domestic plant biomass burning > ambient urban air, possibly with differential toxicity triggers, and showed that the carbonaceous fractions (organic carbon, OC; elemental carbon, EC) and redox-active transition metals (V, Ni, Cr) assisted by water-soluble ions (Ca2+, Mg2+, F-, Cl-) might play important roles in inducing cellular reactive organic species (ROS) production, causing oxidative stress and inflammation, resulting in cell injury and apoptosis, and thus damaging human health. Coupled with the source apportionment results of typical urban ambient air PM2.5 in eastern China, reducing toxic PM2.5 from these anthropogenic combustions will be greatly beneficial to public health. In addition to the air pollution control measures that have been implemented, like strengthening the vehicle emission standards, switching energy from coal to gas and electricity, and controlling the open incineration of agricultural straws, further methods could be considered, especially by preferentially reducing the diesel exhaust, lessening the coal combustion by replacement with low-ash clean coals, and depressing the rural crop straw biomass burning emissions.

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations-the air pollution outcome generally causing the largest monetized health damages-attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.

PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

To obtain a thorough knowledge of PM2. 5 chemical composition and its impact on aerosol optical properties across China, existing field studies conducted after the year 2000 are reviewed and summarized in terms of geographical, interannual and seasonal distributions. Annual PM2. 5 was up to 6 times the National Ambient Air Quality Standards (NAAQS) in some megacities in northern China. Annual PM2. 5 was higher in northern than southern cities, and higher in inland than coastal cities. In a few cities with data longer than a decade, PM2. 5 showed a slight decrease only in the second half of the past decade, while carbonaceous aerosols decreased, sulfate (SO42−) and ammonium (NH4+) remained at high levels, and nitrate (NO3−) increased. The highest seasonal averages of PM2. 5 and its major chemical components were typically observed in the cold seasons. Annual average contributions of secondary inorganic aerosols to PM2. 5 ranged from 25 to 48 %, and those of carbonaceous aerosols ranged from 23 to 47 %, both with higher contributions in southern regions due to the frequent dust events in northern China. Source apportionment analysis identified secondary inorganic aerosols, coal combustion and traffic emission as the top three source factors contributing to PM2. 5 mass in most Chinese cities, and the sum of these three source factors explained 44 to 82 % of PM2. 5 mass on annual average across China. Biomass emission in most cities, industrial emission in industrial cities, dust emission in northern cities and ship emission in coastal cities are other major source factors, each of which contributed 7–27 % to PM2. 5 mass in applicable cities. The geographical pattern of scattering coefficient (bsp) was similar to that of PM2. 5, and that of aerosol absorption coefficient (bap) was determined by elemental carbon (EC) mass concentration and its coating. bsp in ambient condition of relative humidity (RH)  =  80 % can be amplified by about 1.8 times that under dry conditions. Secondary inorganic aerosols accounted for about 60 % of aerosol extinction coefficient (bext) at RH greater than 70 %. The mass scattering efficiency (MSE) of PM2. 5 ranged from 3.0 to 5.0 m2 g−1 for aerosols produced from anthropogenic emissions and from 0.7 to 1.0 m2 g−1 for natural dust aerosols. The mass absorption efficiency (MAE) of EC ranged from 6.5 to 12.4 m2 g−1 in urban environments, but the MAE of water-soluble organic carbon was only 0.05 to 0.11 m2 g−1. Historical emission control policies in China and their effectiveness were discussed based on available chemically resolved PM2. 5 data, which provides the much needed knowledge for guiding future studies and emissions policies.

To obtain a thorough knowledge of PM2.5 chemical composition and its impact on aerosol optical properties across China, existing field studies conducted after the year 2000 are reviewed and summarized in terms of geographical, interannual and seasonal distributions. Annual PM2.5 was up to 6 times the National Ambient Air Quality Standards (NAAQS) in some megacities in northern China. Annual PM2.5 was higher in northern than southern cities, and higher in inland than coastal cities. In a few cities with data longer than a decade, PM2.5 showed a slight decrease only in the second half of the past decade, while carbonaceous aerosols decreased, sulfate (SO42-) and ammonium (NH4+) remained at high levels, and nitrate (NO3-) increased. The highest seasonal averages of PM2.5 and its major chemical components were typically observed in the cold seasons. Annual average contributions of secondary inorganic aerosols to PM2.5 ranged from 25 to 48 %, and those of carbonaceous aerosols ranged from 23 to 47 %, both with higher contributions in southern regions due to the frequent dust events in northern China. Source apportionment analysis identified secondary inorganic aerosols, coal combustion and traffic emission as the top three source factors contributing to PM2.5 mass in most Chinese cities, and the sum of these three source factors explained 44 to 82 % of PM2.5 mass on annual average across China. Biomass emission in most cities, industrial emission in industrial cities, dust emission in northern cities and ship emission in coastal cities are other major source factors, each of which contributed 7–27 % to PM2.5 mass in applicable cities.The geographical pattern of scattering coefficient (bsp) was similar to that of PM2.5, and that of aerosol absorption coefficient (bap) was determined by elemental carbon (EC) mass concentration and its coating. bsp in ambient condition of relative humidity (RH) = 80 % can be amplified by about 1.8 times that under dry conditions. Secondary inorganic aerosols accounted for about 60 % of aerosol extinction coefficient (bext) at RH greater than 70 %. The mass scattering efficiency (MSE) of PM2.5 ranged from 3.0 to 5.0 m2 g-1 for aerosols produced from anthropogenic emissions and from 0.7 to 1.0 m2 g-1 for natural dust aerosols. The mass absorption efficiency (MAE) of EC ranged from 6.5 to 12.4 m2 g-1 in urban environments, but the MAE of water-soluble organic carbon was only 0.05 to 0.11 m2 g-1. Historical emission control policies in China and their effectiveness were discussed based on available chemically resolved PM2.5 data, which provides the much needed knowledge for guiding future studies and emissions policies.

Purpose The paper presents new and updated datasets for the operation of fossil-fuelled passenger cars. These are intended to be used either as background processes or in the comparative assessment of transport options. Central goals were to achieve a high level of consistency, transparency and flexibility for a representative range of current vehicle sizes, emission standards and fuel types, and to make a clear definition between exhaust and non-exhaust emissions. The latter is an important contribution to studies focusing on hybrid and electric vehicles. Methods The datasets are the direct development of those available in ecoinvent v2 and are largely based on updated versions of the same sources. The datasets address petrol, diesel and natural gas vehicle fuels. The number of datasets was increased to cover small, medium and large vehicles. Other data sources were used in order to fill data gaps and to balance inconsistencies, particularly for the natural gas vehicles. Parameterisation was incorporated via the ecoeditor tool. This allows the datasets to be adapted for use as foreground processes and also increases transparency. An important method used was to observe the trends in fuel consumption and emissions across all sizes and emission standards simultaneously so that consistency would be achieved across the whole range of vehicles. Non-exhaust emissions were made dependent on vehicle weight and thereby independent of vehicle type. Results and discussion Some significant changes in individual emission factors between the v2 and v3 datasets was shown. This can be explained by a combination of corrections, updates based on more recent versions of the data sources, and attempts to make the datasets consistent to each other. This has also meant that the non-exhaust emissions are readily definable in terms of brake, tyre and road wear as a factor of vehicle weight, with the intention that this data can be applied to passenger vehicles of all technologies. Conclusions Fuel consumption, emission factors and infrastructure demand have been improved, extended and updated for petrol, diesel and natural gas vehicles adhering to the Euro 3, 4 and 5 emissions standards. Using the ecoeditor tool, significant parameterisation was included which has made the datasets far more flexible, consistent and transparent. The clear definition of non-exhaust emissions means that these can easily be applied to studies on hybrid and electric vehicles.