Different approaches to explore the impact of COVID-19 lockdowns on carbonaceous aerosols at a European rural background site.

To prevent the fast spread of COVID-19, worldwide restrictions have been put in place, leading to a reduction in emissions from most anthropogenic sources. In this study, the impact of COVID-19 lockdowns on elemental (EC) and organic (OC) carbon was explored at a European rural background site combining different approaches: - "Horizontal approach (HA)" consists of comparing concentrations of pollutants measured at 4 m a.g.l. during pre-COVID period (2017-2019) to those measured during COVID period (2020-2021); - "Vertical approach (VA)" consists of inspecting the relationship between OC and EC measured at 4 m and those on top (230 m) of a 250 m-tall tower in Czech Republic. The HA showed that the lockdowns did not systematically result in lower concentrations of both carbonaceous fractions unlike NO2 (25 to 36 % lower) and SO2 (10 to 45 % lower). EC was generally lower during the lockdowns (up to 35 %), likely attributed to the traffic restrictions whereas increased OC (up to 50 %) could be attributed to enhanced emissions from the domestic heating and biomass burning during this stay-home period, but also to the enhanced concentration of SOC (up to 98 %). EC and OC were generally higher at 4 m suggesting a greater influence of local sources near the surface. Interestingly, the VA revealed a significantly enhanced correlation between EC and OC measured at 4 m and those at 230 m (R values up to 0.88 and 0.70 during lockdown 1 and 2, respectively), suggesting a stronger influence of aged and long distance transported aerosols during the lockdowns. This study reveals that lockdowns did not necessarily affect aerosol absolute concentrations but it certainly influenced their vertical distribution. Therefore, analyzing the vertical distribution can allow a better characterization of aerosol properties and sources at rural background sites, especially during a period of significantly reduced human activities.

Characteristics and sources of carbonaceous aerosols in a semi-arid city: Quantifying anthropogenic and meteorological impacts.

Carbonaceous aerosols have great adverse impacts on air quality, human health, and climate. However, there is a limited understanding of carbonaceous aerosols in semi-arid areas. The correlation between carbonaceous aerosols and control measures is still unclear owing to the insufficient information regarding meteorological contribution. To reveal the complex relationship between control measures and carbonaceous aerosols, offline and online observations of carbonaceous aerosols were conducted from October 8, 2019 to October 7, 2020 in Hohhot, a semi-arid city. The characteristics and sources of carbonaceous aerosols and impacts of anthropogenic emissions and meteorological conditions were studied. The annual mean concentrations (± standard deviation) of fine particulate matter (PM2.5), organic carbon (OC), and elemental carbon (EC) were 42.81 (±40.13), 7.57 (±6.43), and 2.25 (±1.39) µg m-3, respectively. The highest PM2.5 and carbonaceous aerosol concentrations were observed in winter, whereas the lowest was observed in summer. The result indicated that coal combustion for heating had a critical role in air quality degradation in Hohhot. A boost regression tree model was applied to quantify the impacts of anthropogenic emissions and meteorological conditions on carbonaceous aerosols. The results suggested that the anthropogenic contributions of PM2.5, OC, and EC during the COVID-19 lockdown period were 53.0, 15.0, and 2.36 µg m-3, respectively, while the meteorological contributions were 5.38, 2.49, and -0.62 µg m-3, respectively. Secondary formation caused by unfavorable meteorological conditions offset the emission reduction during the COVID-19 lockdown period. Coal combustion (46.4% for OC and 35.4% for EC) and vehicular emissions (32.0% for OC and 50.4% for EC) were the predominant contributors of carbonaceous aerosols. The result indicated that Hohhot must regulate coal use and vehicle emissions to reduce carbonaceous aerosol pollution. This study provides new insights and a comprehensive understanding of the complex relationships between control strategies, meteorological conditions, and air quality.

PM2.5 carbonaceous components and mineral dust at a COALESCE network site - Bhopal, India: Assessing the impacts of COVID-19 lockdowns on site-specific optical properties.

Thermal elemental carbon (EC), optical black carbon (BC), organic carbon (OC), mineral dust (MD), and 7-wavelength optical attenuation of 24-hour ambient PM2.5 samples were measured/estimated at a regionally representative site (Bhopal, central India) during a business-as-usual year (2019) and the COVID-19 lockdowns year (2020). This dataset was used to estimate the influence of emissions source reductions on the optical properties of light-absorbing aerosols. During the lockdown period, the concentration of EC, OC, BC880 nm, and PM2.5 increased by 70 % ± 25 %, 74 % ± 20 %, 91 % ± 6 %, and 34 % ± 24 %, respectively, while MD concentration decreased by 32 % ± 30 %, compared to the same time period in 2019. Also, during the lockdown period, the estimated absorption coefficient (babs) and mass absorption cross-section (MAC) values of Brown Carbon (BrC) at 405 nm were higher (42 % ± 20 % and 16 % ± 7 %, respectively), while these quantities for MD, i.e., babs-MD and MACMD values were lower (19 % ± 9 % and 16 % ± 10 %), compared to the corresponding period during 2019. Also, babs-BC-808 (115 % ± 6 %) and MACBC-808 (69 % ± 45 %) values increased during the lockdown period compared with the corresponding period during 2019. It is hypothesized that although anthropogenic emissions (chiefly industrial and vehicular) reduced drastically during the lockdown period compared to the business-as-usual period, an increase in the values of optical properties (babs and MAC) and concentrations of BC and BrC, were likely due to the increased local and regional biomass burning emissions during this period. This hypothesis is supported by the CBPF (Conditional Bivariate Probability Function) and PSCF (Potential Source Contribution Function) analyses for BC and BrC.

Potential pathways to reach energy-related CO2 emission peak in China: analysis of different scenarios.

The prevalence of global unilateralism and the shock of COVID-19 brought considerable uncertainty to China's economic development. Consequently, policy selection related to the economy, industry, and technology is expected to significantly impact China's national economic potential and carbon emission mitigation. This study used a bottom-up energy model to assess the future energy consumption and CO2 emission trend before 2035 under three scenarios: a high-investment scenario (HIS), a medium-growth scenario (MGS), and an innovation-driven scenario (IDS). These were also used to predict the energy consumption and CO2 emission trend for the final sectors and calculate each sector's mitigation contribution. The main findings were as follows. Firstly, under HIS, China would achieve its carbon peak in 2030, with 12.0 Gt CO2. Moderately lowering the economic growth rate to support the low-carbon transition of the economy by boosting the development of the low-carbon industry and speeding up the employment of key low-carbon technologies to improve energy efficiency and optimize energy structure in the final sectors, the MGS and the IDS would achieve carbon peak approximately in 2025, with a peak of 10.7 Gt CO2 for the MGS and 10.0 Gt CO2 for the IDS. Several policy recommendations were proposed to meet China's nationally determined contribution targets: instigating more active development goals for each sector to implement the "1+N" policy system, taking measures to accelerate the R&D, boosting the innovation and application of key low-carbon technologies, strengthening economic incentives, forming an endogenous driving force for market-oriented emission reduction, and assessing the climate impacts of new infrastructure projects.

Uncertainty quantification of CO2 emissions from China's civil aviation industry to 2050.

To mitigate aviation's carbon emissions of the aviation industry, the following steps are vital: accurately quantifying the carbon emission path by considering uncertainty factors, including transportation demand in the post-COVID-19 pandemic period; identifying gaps between this path and emission reduction targets; and providing mitigation measures. Some mitigation measures that can be employed by China's civil aviation industry include the gradual realization of large-scale production of sustainable aviation fuels and transition to 100% sustainable and low-carbon sources of energy. This study identified the key driving factors of carbon emissions by using the Delphi Method and set scenarios that consider uncertainty, such as aviation development and emission reduction policies. A backpropagation neural network and Monte Carlo simulation were used to quantify the carbon emission path. The study results show that China's civil aviation industry can effectively help the country achieve its carbon peak and carbon neutrality goals. However, to achieve the net-zero carbon emissions goal of global aviation, China needs to reduce its emissions by approximately 82%-91% based on the optimal emission scenario. Thus, under the international net-zero target, China's civil aviation industry will face significant pressure to reduce its emissions. The use of sustainable aviation fuels is the best way to reduce aviation emissions by 2050. Moreover, in addition to the application of sustainable aviation fuel, it will be necessary to develop a new generation of aircraft introducing new materials and upgrading technology, implement additional carbon absorption measures, and make use of carbon trading markets to facilitate China's civil aviation industry's contribution to reduce climate change.

[Variations in PM2.5 Composition and Sources During 2020-2021 COVID-19 Epidemic Periods in Nanjing].

To understand the changes in chemical composition and sources of PM2.5 under the extreme reduction background during the COVID-19 epidemic periods in Nanjing, hourly observation results of PM2.5 components (water-soluble inorganic ions, carbonaceous components, and inorganic elements) of two epidemic events from January to March 2020 and June to August 2021 were analyzed. In comparison to that during pre-epidemic periods, the concentration of NO3- during the two epidemic control periods decreased by 52.9% and 43.0%, respectively, which was larger than the decreases in NH4+(46.4% and 31.6%) and SO42-(33.8% and 16.5%). Since the observation site was located close to a main road, the decrease in elemental carbon (EC, 35.4% and 20.6%) was higher than that in organic carbon (OC, 11.1% and 16.2%). In reference to the variations in the characteristic ratios of the bulk components mentioned above, the epidemic control showed a more substantial influence on traffic emissions than industrial activities. The concentration time series of PM2.5 major components over the epidemic periods indicated that NOx from local traffic emissions had substantial contributions to the formation of NO3-, which led to local short-term PM2.5 pollution. In addition, the positive matrix factorization (PMF) model was used to analyze the hourly observation data of PM2.5 components. The seven identified factors were linked with metallurgy, firework and firecracker combustions, road traffic emissions, coal combustion, dust resuspension, secondary sulfate, and secondary nitrate. Because the nitrate was unstable under high temperature, the contribution of secondary nitrate to PM2.5 during the epidemic control period of 2021 (summer, 21.2%) was much lower than that during the epidemic control period of 2020 (winter, 60.6%); however, the formation of secondary components always dominated the contribution of PM2.5 sources. Therefore, emissions of NOx and SO2 should be further controlled to continuously reduce ambient PM2.5 concentrations in Chinese cities.

Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China.

Studying the spatiotemporal evolution of carbon emissions from the perspective of major function-oriented zones (MFOZs) is crucial for making a carbon reduction policy. However, most previous research has ignored the spatial characteristics and MFOZ influence. Using statistical and spatial analysis tools, we explored the spatiotemporal characteristics of carbon emissions in Guangdong Province from 2001 to 2021. The following results were obtained: (1) Carbon emissions fluctuated from 2020 to 2021 because of COVID-19. (2) Over the last 20 years, the proportion of carbon emissions from urbanization development zones (UDZs) has gradually decreased, whereas those of the main agricultural production zones (MAPZs) and key ecological function zones (KEFZs) have increased. (3) Carbon emissions efficiency differed significantly among the three MFOZs. (4) Carbon emissions from coastal UDZs were increasingly apparent; however, the directional characteristics of MAPZ and KEFZ emissions were not remarkable. (5) Carbon transfer existed among the three kinds of MFOZs, resulting in the economy and carbon emissions being considerably misaligned across Guangdong Province. These results indicated that the MFOZ is noteworthy in revealing how carbon emissions evolved. Furthermore, spatiotemporal characteristics, especially spatial characteristics, can help formulate carbon reduction policies for realizing carbon peak and neutrality goals in Guangdong Province.

Black carbon over tropical Indian coast during the COVID-19 lockdown: inconspicuous role of coastal meteorology.

Black carbon (BC) aerosols critically impact the climate and hydrological cycle. The impact of anthropogenic emissions and coastal meteorology on BC dynamics, however, remains unclear over tropical India, a globally identified hotspot. In this regard, we have performed in situ measurements of BC over a megacity (Chennai, 12° 59' 26.5″ N, 80° 13' 51.8″ E) on the eastern coast of India during January-June 2020, comprising the period of COVID-19-induced strict lockdown. Our measurements revealed an unprecedented reduction in BC concentration by an order of magnitude as reported by other studies for various other pollutants. This was despite having stronger precipitation during pre-lockdown and lesser precipitation washout during the lockdown. Our analyses, taking mesoscale dynamics into account, unravels stronger BC depletion in the continental air than marine air. Additionally, the BC source regime also shifted from a fossil-fuel dominance to a biomass burning dominance as a result of lockdown, indicating relative reduction in fossil fuel combustion. Considering the rarity of such a low concentration of BC in a tropical megacity environment, our observations and findings under near-natural or background levels of BC may be invaluable to validate model simulations dealing with BC dynamics and its climatic impacts in the Anthropocene.

Spatiotemporal Analysis of Black Carbon Sources: Case of Santiago, Chile, under SARS-CoV-2 Lockdowns.

BACKGROUND: The SARS-CoV-2 pandemic has temporarily decreased black carbon emissions worldwide. The use of multi-wavelength aethalometers provides a quantitative apportionment of black carbon (BC) from fossil fuels (BCff) and wood-burning sources (BCwb). However, this apportionment is aggregated: local and non-local BC sources are lumped together in the aethalometer results. METHODS: We propose a spatiotemporal analysis of BC results along with meteorological data, using a fuzzy clustering approach, to resolve local and non-local BC contributions. We apply this methodology to BC measurements taken at an urban site in Santiago, Chile, from March through December 2020, including lockdown periods of different intensities. RESULTS: BCff accounts for 85% of total BC; there was up to an 80% reduction in total BC during the most restrictive lockdowns (April-June); the reduction was 40-50% in periods with less restrictive lockdowns. The new methodology can apportion BCff and BCwb into local and non-local contributions; local traffic (wood burning) sources account for 66% (86%) of BCff (BCwb). CONCLUSIONS: The intensive lockdowns brought down ambient BC across the city. The proposed fuzzy clustering methodology can resolve local and non-local contributions to BC in urban zones.

Synergistic Effects of International Oil Price Fluctuations and Carbon Tax Policies on the Energy-Economy-Environment System in China.

Catalyzed by COVID-19 and the Russia-Ukraine conflict, oil prices fluctuate dramatically on the worldwide market. Both international oil price changes and carbon tax policies have a direct impact on energy costs, thus influencing energy security and emission reduction impacts. Therefore, assessing the interaction effects of international oil price variations and carbon tax policies can assist in resolving the competing challenges of energy security and carbon emission reduction. The impact of international oil price fluctuations on China's energy-economic-environment system under the baseline scenario and carbon taxation scenario is analyzed by constructing a computable general equilibrium model comprising six modules: production, trade, institutions, price, environment, and equilibrium. The findings indicate that, in addition to reducing high-carbon energy consumption and increasing demand for clean electricity, rising international oil prices have a negative effect on real GDP, resulting in lower output in sectors other than construction, and a positive effect on the environmental system by driving carbon emission reductions. In contrast, decreasing international oil prices have the opposite effect. Nevertheless, the impact of rising and decreasing international oil prices is asymmetrical, with the positive shock effect being smaller than the negative. The carbon tax policy can effectively offset the increase in carbon emissions caused by the decline in international oil prices, which is conducive to promoting the development of clean energy, while simultaneously causing an increase in product prices and arousing a contraction in consumer demand, which has a limited negative impact on the macroeconomy.

Comparison of concentrations of chemical species and emission sources PM2.5 before pandemic and during pandemic in Krakow, Poland.

Observations of air pollution in Krakow have shown that air quality has been improved during the last decade. In the presented study two factors affecting the physicochemical characteristic of PM2.5 fraction at AGH station in Krakow were observed. One is the ban of using solid fuels for heating purposes and the second is COVID-19 pandemic in Krakow. The PM2.5 fraction was collected during the whole year every 3rd day between 2nd March 2020 and 28th February 2021 at AGH station in Krakow. In total 110 PM2.5 fraction samples were collected. The chemical composition was determined for these samples. The elemental analysis was performed by energy dispersive X-ray fluorescence (EDXRF) technique, ions analysis was performed by ion chromatography (IC) and black carbon by optical method. In order to identify the emission sources the positive matrix factorization (PMF) was used. The results of such study were compared to similar analysis performed for PM2.5 for the period from June 2018 to May 2019 at AGH station in Krakow. The PM2.5 concentration dropped by 25% in 2020/2021 in comparison to 2018/2019 at this station. The concentrations of Si, K, Fe, Zn and Pb were lowering by 43-64% in the year 2020/2021 in comparison to 2018/2019. Cu, Mn, Zn and Pb come from mechanical abrasion of brakes and tires while Ti, Fe, Mn and Si are crustal species. They are the indicators of road dust (non-exhaust traffic source). Moreover, the annual average contribution of traffic/industrial/soil/construction work source was reduced in 2020/2021 in comparison to 2018/2019. As well the annual average contribution of fuels combustion was declining by 22% in 2020/2021 in comparison to 2018/2019. This study shows that the ban and lockdown, during COVID-19 pandemic, had significant impact on the characteristic of air pollution in Krakow.

Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling.

Hourly PM2.5 speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM2.5 components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM2.5 water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMFwhole). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMFwhole solution. After removing event data, PMF modeling was conducted for individual months (PMFmonth) and the 4-year period (PMF4-year), respectively. PMFmonth solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF4-year solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

Spatial-Temporal Characteristics and Driving Factors of the Eco-Efficiency of Tourist Hotels in China.

At present, COVID-19 is seriously affecting the economic development of the hotel industry, and at the same time, the world is vigorously calling for "carbon emission mitigation". Under these two factors, tourist hotels are in urgent need of effective tools to balance economic and social contributions with ecological and environmental impacts. Therefore, this paper takes Chinese tourist hotels as the research object and constructs a research framework for Chinese tourist hotels by constructing a Super-SBM Non-Oriented model. We measured the economic efficiency and eco-efficiency of Chinese tourist hotels from 2000 to 2019; explored spatial-temporal evolution patterns of their income, carbon emissions, eco-efficiency, and economic efficiency through spatial hotspot analysis and center of gravity analysis; and identified the spatial agglomeration characteristics of such hotels through the econometric panel Tobit model to identify the different driving factors inside and outside the tourist hotel system. The following results were obtained: (1) the eco-efficiency of China's tourist hotels is higher than the economic efficiency, which is in line with the overall Kuznets curve theory, but the income and carbon emissions have not yet been decoupled; (2) most of China's tourist hotels are crudely developed with much room for improving the economic efficiency, and most of the provincial and regional tourist hotels are at a low-income level, but the carbon emissions are still on the increase; and (3) income, labor, carbon emissions, waste emissions, and water consumption are the internal drivers of China's tourist hotels, while industrial structure, urbanization rate, energy efficiency, and information technology are the external drivers of China's tourist hotels. The research results provide a clear path for the reduction in carbon emissions and the improvement of the eco-efficiency of Chinese tourist hotels. Under the backdrop of global climate change and the post-COVID-19 era, the research framework and conclusions provide references for countries with new economies similar to China and countries that need to quickly restore the hotel industry.

Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study.

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO2 emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO2 emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO2 increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO2 emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.

Impact of COVID-19 lockdown on carbonaceous aerosols in a polluted city: Composition characterization, source apportionment, influence factors of secondary formation.

Carbonaceous fractions throughout the normal period and lockdown period (LP) before and during COVID-19 outbreak were analyzed in a polluted city, Zhengzhou, China. During LP, fine particulate matters, elemental carbon (EC), and secondary organic aerosol (SOC) concentrations fell significantly (29%, 32% and 21%), whereas organic carbon (OC) only decreased by 4%. Furthermore, the mean OC/EC ratio increased (from 3.8 to 5.4) and the EC fractions declined dramatically, indicating a reduction in vehicle emission contribution. The fact that OC1-3, EC, and EC1 had good correlations suggested that OC1-3 emanated from primary emissions. OC4 was partly from secondary generation, and increased correlations of OC4 with OC1-3 during LP indicated a decrease in the share of SOC. SOC was more impacted by NO2 throughout the research phase, thereby the concentrations were lower during LP when NO2 levels were lower. SOC and relative humidity (RH) were found to be positively associated only when RH was below 80% and 60% during the normal period (NP) and LP, respectively. SOC, Coal combustion, gasoline vehicles, biomass burning, diesel vehicles were identified as major sources by the Positive Matrix Factorization (PMF) model. Contribution of SOC apportioned by PMF was 3.4 and 3.0 µg/m3, comparable to the calculated findings (3.8 and 3.0 µg/m3) during the two periods. During LP, contributions from gasoline vehicles dropped the most, from 47% to 37% and from 7.1 to 4.3 µg/m3, contribution of biomass burning and diesel vehicles fell by 3% (0.6 µg/m3) and 1% (0.4 µg/m3), and coal combustion concentrations remained nearly constant. The findings of this study highlight the immense importance of anthropogenic source reduction in carbonaceous component variations and SOC generation, and provide significant insight into the temporal variations and sources of carbonaceous fractions in polluted cities.

Significant reduction in atmospheric organic and elemental carbon in PM2.5 in 2+26 cities in northern China.

To better understand the change characteristics and reduction in organic carbon (OC) and elemental carbon (EC) in particulate matter (PM) with a diameter of ≤2.5 µm (PM2.5) driven by the most stringent clean air policies and pandemic-related lockdown measures in China, a comprehensive field campaign was performed to measure the carbonaceous components in PM2.5 on an hourly basis via harmonized analytical methods in the Beijing-Tianjin-Hebei and its surrounding region (including 2 + 26 cities) from January 1 to December 31, 2020. The results indicated that the annual average concentrations of OC and EC reached as low as 6.6 ± 5.7 and 1.8 ± 1.9 µg/m3, respectively, lower than those obtained in previous studies, which could be attributed to the effectiveness of the Clean Air Action Plan and the impact of the COVID-19-related lockdown measures implemented in China. Marked seasonal and diurnal variations in OC and EC were observed in the 2 + 26 cities. Significant correlations (p < 0.001) between OC and EC were found. The annual average secondary OC levels level ranged from 1.8-5.4 µg/m3, accounting for 37.7-73.0% of the OC concentration in the 2 + 26 cities estimated with the minimum R squared method. Based on Interagency Monitoring of Protected Visual Environments (IMPROVE) algorithms, the light extinction contribution of carbonaceous PM to the total amount reached 21.1% and 26.0% on average, suggesting that carbonaceous PM played a less important role in visibility impairment than did the other chemical components in PM2.5. This study is expected to provide an important real-time dataset and in-depth analysis of the significant reduction in OC and EC in PM2.5 driven by both the Clean Air Action Plan and COVID-19-related lockdown policies over the past few years, which could represent an insightful comparative case study for other developing countries/regions facing similar carbonaceous PM pollution.

Feasible Distributed Energy Supply Options for Household Energy Use in China from a Carbon Neutral Perspective.

This contribution firstly proposed the concept of annual average power generation hours and analyzed per capita energy consumption, carbon emission, and the human development index from a macro perspective. On this basis, we compared the average household electrical energy consumption of urban and rural residents based on the data from CGSS-2015 from a micro perspective. The results show the positive correlation between carbon emissions per capita and the human development index and China's regional imbalance characteristics between household electricity consumption and renewable energy distribution. Therefore, the distributed energy supply system is proposed as an effective complement to centralized power generation systems and is the key to synergizing human development and carbon emissions in China. Moreover, we analyzed the characteristics of distributed energy supply systems in the context of existing energy supply systems, pointing out the need to fully use solar energy and natural gas. Finally, two types of typical distributed energy supply systems are proposed for satisfying the household energy requirements in remote or rural areas of western and the eastern or coastal areas of China, respectively. Two typical distributed energy systems integrate high-efficiency energy conversion, storage, and transfer devices such as electric heat pumps, photovoltaic thermal, heat and electricity storage, and fuel cells.

Does government intervention affect CO2 emission reduction effect of producer service agglomeration? Empirical analysis based on spatial Durbin model and dynamic threshold model.

Achieving carbon peak and carbon neutrality is an inherent requirement for countries to promote green recovery and transformation of the global economy after the COVID-19 pandemic. As "a smoke-free industry," producer services agglomeration (PSA) may have significant impacts on CO2 emission reduction. Therefore, based on the nightlight data to calculate the CO2 emissions of 268 cities in China from 2005 to 2017, this study deeply explores the impact and transmission mechanism of PSA on CO2 emissions by constructing dynamic spatial Durbin model and intermediary effect model. Furthermore, the dynamic threshold model is used to analyze the nonlinear characteristics between PSA and CO2 emissions under different degrees of government intervention. The results reveal that: (1) Generally, China's CO2 emissions are path-dependent in the time dimension, showing a "snowball effect." PSA significantly inhibits CO2 emissions, but heterogeneous influences exist in different regions, time nodes, and sub-industries; (2) PSA can indirectly curb CO2 emissions through economies of scale, technological innovation, and industrial structure upgrading. (3) The impact of PSA on China's CO2 emissions has an obvious double threshold effect under different degree of government intervention. Accordingly, the Chinese government should increase the support for producer services, dynamically adjust industrial policies, take a moderate intervention, and strengthen market-oriented reform to reduce CO2 emissions. This study opens up a new path for the low-carbon economic development and environmental sustainability, and also fills in the theoretical gaps on these issues. The findings and implications will offer instructive guideline for early achieving carbon peak and carbon neutrality.

Does the Application of ICTs Improve the Efficiency of Agricultural Carbon Reduction? Evidence from Broadband Adoption in Rural China.

Based on the Environmental Kuznets Curve (EKC) hypothesis, this paper examines whether rural broadband adoption affects agricultural carbon reduction efficiency (ACRE), using panel data from 30 Chinese provinces from 2011 to 2019. This paper achieves a measurement of ACRE by taking the carbon sink of agricultural as one of the desired outputs and using a Slacks-Based Measure (SBM) model and the global Malmquist-Luenberger (GML) index. The results show that: (1) Rural broadband adoption has a positive effect on ACRE. The relationship between the income of rural residents and ACRE was an inverted U-shaped, which confirms the EKC hypothesis. (2) Land transfer has a significant promoting effect on the relationship between rural broadband adoption and ACRE. When the land transfer rate is high, the positive effect of broadband adoption is obvious. (3) The positive effect of broadband adoption on ACRE was more obvious when farmers invested more in production equipment, that is to say, it has a significant positive moderating effect. As farmers in many developing countries suffer from increasingly frequent and severe extreme weather events, we believe that the results of this study also have implications for the implementation of agricultural carbon reduction and smart agricultural equipment roll-out in many countries.

Recent Developments in the Determination of PM2.5 Chemical Composition.

Fine particulate matter (named PM2.5) has become a prominent and dangerous form of air pollution. The chemical composition of PM2.5 mainly includes inorganic elements, water soluble ions, elemental carbon (EC), organic carbon (OC), and organic compounds. The detection method for inorganic elements mainly includes X ray fluorescence, inductively coupled plasma-atomic emission spectrometry, and inductively coupled plasma mass spectrometry. As for water soluble ions, ion chromatography is the most common detection method. EC and OC are usually detected by carbon analyzer. The organic compounds are determined by gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. In this paper, the merits and drawbacks of each analytical methods for the determination of PM2.5 chemical composition are summarized. This review also includes our discussion on the improvement of the analytical accuracy for the determination of PM2.5 chemical composition owing to the development of reference materials.