Changes In Emissions Research Articles

Discrepant Global Surface Ozone Responses to Emission- and Heatwave-Induced Regime Shifts.

Heatwaves have substantial but poorly quantified impacts on surface ozone photochemical regimes. As heatwaves of increasing severity occur, communities face more serious exposure to ozone, necessitating a more comprehensive understanding of the impact of heatwaves on the nonlinear response of ozone to its precursors for guiding policies in emission reductions. Here we estimate the spatiotemporal evolution of global ozone chemistry based on machine learning and in situ observations and show that emission changes and heatwaves alter ozone photochemical regimes, leading to diverse ozone changes across regions. Sustained emission reductions in East Asia decreased the ozone formation sensitivity to formaldehyde (HCHO) and fine particulate matter (PM2.5), counteracting the adverse high-temperature effect. Quantified results reveal that heatwaves increased the sensitivity of ozone to HCHO and PM2.5, enhancing their positive contributions and causing increased ozone trends across most regions, with a global average anomaly of 9.4 μg/m3. Meanwhile, heatwave-induced PM2.5 anomalies concentrated in wildfire-risk zones, coupled with increased HCHO, elevated downwind ozone levels. Specifically, the effects in wildfire-endangered western Canada and heatwave-exposed southeastern United States contributed to a chemically driven ozone increase of 0.18 μg/m3/month in Northern America. Our results demonstrate that more targeted and substantial regulation of volatile organic compounds will be beneficial in mitigating future intensifying climate penalty effects.

Comparative Time Series Analysis of SARIMA, LSTM, and GRU Models for Global SF6 Emission Management System

To maintain the balance of the atmosphere, the amount of change in greenhouse gas emissions must be under control. In order to create a management system and take forward-looking steps in this regard, there is no concrete data other than prediction models today. The success of prediction methods is better understood by comparing multiple methods. This research estimates the changes in the emissions of Sulfur Hexafluoride gas (SF6) in the atmosphere using Seasonal Autoregressive Integrated Moving Average Model (SARIMA), Long-Short Term Memory Neural Network (LSTM) and Gated Recurrent Unit (GRU) forecast models and compares their accuracies. Focusing on monthly SF6 emission values between 1998; 2023, time series analysis was performed to predict future emission figures. The actual values and forecast results were compared and evaluated with performance criteria such as R2, RMSE, NSE, MAE and MAPE%. The findings of this research highlight a continious upward trend in SF6 emissions and project that emission levels could approximately double from current levels by 2050. During the analysis process, all three methods performed well in estimating global SF6 gas emissions. The LSTM model generally outperformed SARIMA and GRU models, having the lowest MAPE (0.003%), MAE (0.0003), RMSE (0.0003), and R2 (1) values. It also exhibited very high predictive success with an NSE value of 0.9991. Therefore, it was determined to be the most suitable estimation method with the least error. The aim of this study is to contribute scientifically to the reduction strategies of SF6 emissions.

Temperature adaptive passive daytime radiative cooling: The impact of their essential properties on energy performance of different building types

Passive daytime radiative cooling (PDRC) materials can reduce building cooling demands during hot periods but increase heating needs in colder times, known as heating penalties, limiting their practicality. Temperature adaptive (TA) materials offer a solution, yet optimal TA PDRC properties, such as changes in reflectivity or emissivity from high surface temperature (HST) to low surface temperature (LST) states and the switch temperature at which these changes occur, are under-researched. To address this, a wavelength-dependent PDRC model was coupled with EnergyPlus to identify the optimal reflectivity or emissivity in HST and LST states and determine the appropriate switch temperature. Results show that different reflectivity and emissivity values are needed for various climates to maximize TA PDRC benefits. Switch temperatures from 15 °C to 30 °C primarily affect cooling benefits with minimal impact on heating penalties. Additionally, the heating penalty and cooling benefit of TA PDRC vary across building types, requiring alignment between TA PDRC operational characteristics and HVAC systems to optimize advantages. Among building types, malls benefit the most from PDRC application.

Carbon emission reduction potential of land use in typical alpine meadow region in China

Alpine meadows are primarily situated in mountainous areas and plateaus, exhibiting a high sensitivity to both climate fluctuations and human activities. However, understanding the factors influencing land use carbon emissions change in these regions remains complex, with uncertain mechanisms. Thus, it is imperative to accurately grasp the factors affecting carbon emissions in alpine meadows. We conducted a study estimating land use carbon emissions in the Upper Yellow River in Gannan from 1990 to 2020, using the Logarithmic Mean Divisia Index decomposition model and the Tapio decoupling model to identify driving factors of land use carbon emissions. Additionally, we analyzed the correlation between these factors and economic growth. The findings showed that the land use efficiency in the Upper Yellow River in Gannan region improved substantially by 436 %. Particularly, construction land emerged as the primary source of carbon emissions. The land use carbon emission increased with the increase of carbon emission intensity of construction land and the expansion of the size of construction land. Moreover, economic development and land use carbon emissions are weak decoupled. These results suggest that economic development is considered the primary driving force behind the increase in land use carbon emissions in the region. To achieve the decoupling of economic development and land use carbon emissions, we propose to improve land use efficiency by adjusting the industrial structure as well as improving land use efficiency.

Detecting and Tracking β-Amyloid Oligomeric Forms and Dynamics In Vitro by a High-Sensitivity Fluorescent-Based Assay.

Aggregation of β-amyloid protein is a hallmark pathology of the neurodegenerative disorder Alzheimer's disease and proceeds from monomers to insoluble misfolded fibril forms via soluble and highly toxic oligomeric intermediates. Given the dual feature of being the most toxic form of the Aβ aggregate proteome and an early marker of pathogenesis, there is a need for sensitive methods that can be used to detect Aβ oligomers and investigate the dynamics of aggregation. Herein, we describe a method based on the application of an oligomer-sensitive fluorescent chemical probe pTP-TFE combined with the use of a QIAD (Quantitative determination of Interference with Aβ Aggregate Size Distribution) assay to correctly identify Aβ oligomers in high sensitivity. pTP-TFE was evaluated and compared to thioflavin T and pFTAA, the two most widely used amyloid fibril dyes, and shown to be the only probe capable of detecting significant differences across all oligomeric species of β-amyloid. Furthermore, by observing changes in pTP-TFE fluorescence emission over time, we could track the dynamics of oligomer populations and thereby obtain kinetic information on the Aβ42 dynamic aggregation model. Therefore, we have established a highly sensitive, readily available, and simple method for studying β-amyloid protein aggregation dynamics.

Development of a regional carbon assimilation system and its application for estimating fossil fuel carbon emissions in the Yangtze River Delta, China

Urban areas are the largest contributors to global fossil fuel carbon emissions, and controlling urban carbon emissions is critical to addressing climate change and reducing greenhouse gas emissions. However, estimates of urban emissions remain large uncertainties, making it difficult to accurately understand changes in urban carbon emissions and to assess the effectiveness of emission controls. Atmospheric CO2 observations, through data assimilation, can objectively invert changes in urban carbon emissions. In this study, inheriting the inversion method of a global carbon assimilation system of GCASv2, we constructed a regional carbon assimilation system based on a regional 3-D atmospheric chemical transport model WRF-CMAQ, named RegGCAS. We tested the performance of the established assimilation system for winter carbon emissions in the Yangtze River Delta (YRD) by performing a 9 km × 9 km resolution inversion in December 2020, using observations from 6 ground stations and two emission inventories as a priori, respectively. The results show that after assimilating the observations in the YRD region, especially in Suzhou and Hangzhou where there are more observing sites, the two significantly different a priori emissions are effectively converged, with the relative difference being reduced from −45 % and − 36 % of the a priori emissions to −10 % and − 15 % of the a posteriori emissions. Furthermore, the daily changes of the inverted emissions clearly show that weekend emissions are lower than weekday emissions, i.e. the inversion can reveal the weekend effect of emissions changes. This suggests that our system is well established and can be used for estimating fossil fuel carbon emissions during winter. Meanwhile, these results also imply that with our system, when a city has three or more stations, its total emissions can be constrained reliably.

The impact of CO₂ emissions from different types of transport on countries’ logistics efficiency: Case of ITF member countries

This study provides empirical evidence that CO₂ emissions from transport (total and by transport types) have an impact on countries’ logistics efficiency, both in general (Logistics Performance Index) and in terms of its individual indicators (volumes of freight transport (total and by transport types) and total investments in transport infrastructure). The aim is to empirically demonstrate the impact of CO₂ emissions from different modes of transport on the logistics efficiency of countries, specifically using data from 60 International Transport Forum (ITF) member countries. The Granger causality analysis, based on VAR modeling for six periods evaluated by the World Bank (2007, 2010, 2012, 2014, 2016, 2018), demonstrates that in 50% of the countries, changes in CO₂ emissions from transport are the cause of the shifts in the Logistics Performance Index (increasing emissions can impede logistics efficiency). The sample was reduced to these countries for the regression modeling. Regression modeling (with fixed and random effects, Hausman test, for 2002–2021 on panel data for countries in which direct causality was confirmed) showed that a one-unit increase in total CO2 emissions from transport (in general and in air transport) is associated with 0.12 and 0.21 decrease in total investments in transport infrastructure. An increase in road CO2 emissions is associated with a 0.49 decrease in road freight transport (ton-km per thousand units of GDP). An increase in CO2 rail emissions is associated with a 0.15 increase in total investments in transport infrastructure and a 0.12 increase in total freight transport.

Assessing CO2 Reduction Effects Through Decarbonization Scenarios in the Residential and Transportation Sectors: Challenges and Solutions for Japan’s Hilly and Mountainous Areas

Depopulation, aging, and regional decline are becoming increasingly serious issues in Japan’s hilly and mountainous areas. Focusing on mitigating environmental damage and envisioning a sustainable future for these regions, this study examines the potential for reducing CO2 emissions in the residential and transportation sectors by 2050. Bottom-up simulations were used to estimate CO2 emissions. Subsequently, six decarbonization scenarios were formulated, considering various measures from the perspectives of population distribution and technological progress. Based on these scenarios, this study analyzes changes in future population, energy consumption, and CO2 emissions by 2050. The results of this study show the following. (1) Depopulation and aging problems in these regions are expected to become more severe in the future. It is necessary to take action to promote sustainable regional development. (2) Pursuing decarbonization has a positive impact on enhancing regional sustainability; however, maintaining the intensity of measures at the current level could lead to a reduction of only 40% in CO2 emissions per capita by 2050 compared with 2020. (3) Scenarios that strengthen decarbonization measures could achieve a reduction of over 95% by 2050, indicating that carbon neutrality is attainable. However, this will require implementing measures at a higher intensity, especially in the transportation sector.

Food, energy, and water security sustainability; an asymmetric approach for achieving sustainable development goals

AbstractThe world has arguably identified both output and population growth over time. Thus, the demand for agricultural commodities such as biofuels and the prominent challenges associated with sustainable land use, water, and food development all play a significant role in the development process, as all countries strive to achieve the use of different approaches, such as convergence in output per capita, energy use, water availability, and food production, more safely and ecologically. Therefore, this study is led by Access and Neo-Malthusian theories to investigate the effects of food, energy, and water security sustainability for achieving sustainable development goals using annual data covering 1980 to 2020 to estimate the asymmetric autoregressive distributive lag approach. Accordingly, the results revealed that the effects of positive changes in food, energy, and water security on sustainable development differ significantly from those of negative changes. A 1% change in food production, water supply development, or industrial value addition has a significant positive impact on sustainable development; however, a 1% change in energy use and carbon emission, population growth, or depletion of natural resources has substantial adverse effects on sustainable economic development in both the short run and the long run in Nigeria. The findings also revealed that restoring equilibrium convergence to achieve 2030 SDGs requires 16.2% each year. The study recommends adopting innovative systems that control and help manage available human and natural resources in productive sectors and increase value-added and renewable energy production in Nigeria.

Development of an assay for tetracycline detection based on gold nanocluster synthesis on tetracycline monooxygenase: TetX2@AuNCs

Gold nanoclusters were synthesized based on the structure of the TetX2 Monooxygenase enzyme to make a nanocluster based on enzyme structure (TetX2@Au-NCs). Kinetic analysis of TetX2@Au-NC nanozyme revealed that the Km values of TetX2@Au-NCs to both H2O2 and TMB chromogenic substrate components are higher in the absence of tetracycline. Additionally, the Vmax of the nanozyme for TMB increased in the presence of tetracycline. Also, in the presence of H2O2 the Vmax of TetX2@Au-NCs nanozyme for tetracycline was decreased. In order to monitor tetracycline, the peroxidation properties of TetX2@Au-NCs were utilized to change the color of the chromogenic substrate (TMB-H2O2). The catalytic properties of gold nanoclusters were assessed by chemiluminescence reactions, resulting in changes in light emission. Additionally, fluorescence emission changes of TetX2@Au-NCs were monitored at 450 nm. Three different approach including colorimetry, luminescence, and fluorimetry were used to detect tetracycline with detection times and limits of 15 min and 1.3 mM, 1 min and 1.4 mM, and 30 min and 1.6 mM, respectively. Considering changes in temperature, pH, and even high concentrations of the substrate effects on the performance of enzymes, utilizing the TetX2@Au-NCs nanozyme based on TetX2 monooxygenase enzyme proves to be efficient for detecting higher concentrations with increased accuracy and sensitivity.

Factors and structural paths of the changes in carbon emissions in China's provincial construction industries

The changes in the carbon emissions in China's provincial construction industries are of high complexity. It is essential to understand the changes in the construction carbon emissions (CCEs) in China on the provincial scale. This study evaluates the factors and structural paths of the changes in provincial CCEs in China between 2012 and 2017 using the structural path decomposition analysis. The results show that the emission intensity effect and production structure effect contributed greatly to the reduction of CCEs across various regions, while the final demand effect had contrary impacts. The local nonmetallic mineral products industry (c13), metal smelting and pressing industry (c14), and electricity industry (c24) generally contributed significantly to the emission intensity effect, production structure effect, and final demand effect across most regions. The consumption of local c13, c14, and c24 by the construction industry (c27), namely “local c13→c27”, “local c14→c27”, and “local c24→c27” were generally the important structural paths of the CCEs changes across various regions. Nonlocal industries such as Hebei c14 and nonlocal structural paths such as “Hebei c14→c27” contributed substantially to the CCEs changes in many regions such as Beijing. The emission intensity effect, first-order production structure effect, and final demand effect typically dominated the effects of the critical structural paths of the CCEs changes across various regions. This study can help policymakers better understand the changes in China's provincial CCEs to formulate region-specific emission reduction measures and provide a comprehensive reference for related research.

Cooperative Strategies in Transboundary Water Pollution Control: A Differential Game Approach

This paper, based on differential game theory, examines governance models and cooperative strategies for managing cross-border water pollution in regions with uneven economic development. To address cross-regional water pollution, three differential game models are constructed under different scenarios: the Nash noncooperative mechanism, the pollution control cost compensation mechanism, and the collaborative cooperation mechanism. This study analyzes the dynamic changes in pollution emissions, governance investments, and economic returns within each model. The results indicate that the collaborative cooperation mechanism is the most effective, as it significantly reduces pollution emissions, maximizes overall regional benefits, and achieves Pareto optimality. In comparison, the pollution control cost compensation mechanism is suboptimal under certain conditions, while the Nash noncooperative mechanism is the least efficient, resulting in the highest pollution emissions. Furthermore, the research explores the influence of cooperation costs on the selection of governance models. It finds that high cooperation costs reduce local governments’ willingness to engage in collaborative cooperation. However, an appropriate compensation mechanism can effectively encourage less-developed regions to participate. Numerical analysis confirms the dynamic evolution of pollution stocks and economic returns under different models, and provides corresponding policy recommendations. This paper offers theoretical insights and practical guidance for cross-regional water pollution management, highlighting the importance of regional cooperation and cost-sharing in environmental governance.

Unexpectedly strong heat stress induction of monoterpene, methylbutenol, and other volatile emissions for conifers in the cypress family (Cupressaceae)

We investigated the biogenic volatile organic compound (BVOC) emission rates and composition of Cupressaceae species and how the emissions change in response to moderate warming and more severe heat stress. A total of 8 species from 7 distinct Cupressaceae genera were targeted in this study and exposed to laboratory-simulated heatwaves. Each plant was enclosed in a temperature-controlled glass chamber and allowed to equilibrate at 30 °C for 24 h. The temperature was then increased stepwise from 33 °C to 43 °C in 2 °C increments, with each step lasting 2 h, and was finally kept at 45 °C for 12 h. The BVOC emissions were measured periodically using an automated air sampler coupled to a gas chromatograph.Most of the sampled Cupressaceae species (6 out of 8) were low BVOC emitters (<0.3 μgC g−1 h−1) at 30 °C. However, the BVOC emissions of all 8 species increased strongly with temperature, and in most species (5 out of 8), the emissions continued to increase with longer exposure times to heat stress. The largest increase was observed in Thuja occidentalis and Chamaecyparis thyoides, which reached maximum emissions of 350 and 190 μgC g−1 h−1, respectively. Of the different BVOCs, monoterpenes responded most strongly to heat stress, with Q10 temperature coefficients typically ranging between 7.6 and 22, which were significantly greater than the model-predicted value of 2.7. Other BVOCs including sesquiterpenes, C9 aromatics (only detected in Calocedrus decurrens), methylbutenols, and other C5 oxygenates were also induced by heat stress, but generally at a lower magnitude than monoterpenes.Our results indicate that Cupressaceae are a large but typically dormant source of reactive volatile hydrocarbons (mostly monoterpenes) whose emissions can be activated by heat stress. This phenomenon could have important implications for ozone and aerosol formation, air quality, and human health, particularly in urban areas that are prone to heatwaves.

Historical Insights into CO2 Emission Dynamics in Urban Daily Mobility: A Case Study of Lyon’s Agglomeration

CO2 emissions from urban daily mobility play a major role in both environmental degradation, rising economic costs, and sustainability. Reducing these emissions not only advances environmental sustainability but also fosters economic development by enhancing public health, lowering energy consumption, and alleviating the financial strain caused by climate change. Understanding the dynamics of CO2 emissions from urban daily mobility provides valuable historical insights into environmental impacts and economic costs tied to urban development. This study takes a historical perspective, examining changes in CO2 emissions associated with daily mobility in the Lyon agglomeration across two decades, drawing on data from the 1995 and 2006 household travel surveys. Our findings reveal that individual factors such as gender, age, employment status, and income significantly influence CO2 emissions, with males and full-time workers exhibiting higher emissions. Furthermore, household characteristics, including size and vehicle ownership, are critical in shaping emission levels, while urban form variables such as population density and mixed land use demonstrate a negative correlation with emissions, highlighting the importance of urban planning in mitigating CO2 output. The analysis emphasizes that greater accessibility and proximity to essential services are vital in reducing individual emissions. Based on these insights, we discuss the implications for policy design, suggesting targeted strategies to enhance urban mobility, improve public transport accessibility, and promote sustainable urban development. Finally, we outline directions for future research to further explore the intricate relationship between urban characteristics and emissions, ultimately aiming to contribute to the development of effective climate policies.

Carbon Emission for China's Iron and Steel Industry： Peak Scenarios and Neutralization Pathways

To explore the future carbon emission peak scenarios of China's iron and steel industry as well as the effective pathways for carbon emission neutrality, the generalized Divisia index method （GDIM） was first used to analyze the influencing factors of carbon emission changes from 2001 to 2020, and then Monte Carlo simulation was used to conduct a dynamic scenario simulation of the carbon emission evolution trends from 2021 to 2035. The results showed that： ① Economic output and crude steel production were the most important factors contributing to the increase in carbon emission in the iron and steel industry； among the factors contributing to the decrease, the carbon intensity of economic output had the most significant effect, followed by the carbon intensity of production, and the energy consumption per ton of steel and the energy output rate did not have a significant effect on the decrease in carbon emissions. ② Under the scenario BAU, scenario L, and scenario S, the iron and steel industry could achieve carbon emission peaking in 2030, 2025, and 2020, respectively.

Spatial and Temporal Evolution Characteristics of Agricultural Carbon Offset Rate and Prediction of Carbon Offset Potential in the Yangtze River Economic Belt

Based on the panel data of prefecture-level cities and above and provincial-level cities in the Yangtze River Economic Belt, respectively, this study measured the agricultural carbon emissions, carbon sinks, and carbon offset rates from 2006 to 2021 and analyzed their evolution characteristics. Based on the STIRPAT model and ridge regression analysis, this study identified the factors affecting agricultural carbon emissions in the 11 provinces and municipalities in the Yangtze River Economic Zone, combined them with the scenario analysis method to predict the agricultural carbon emissions under the baseline scenario in the period of 2022 to 2025, and analyzed the process of "carbon peaking." Simultaneously, this study predicted the agricultural carbon sinks of 11 provinces and cities from 2022 to 2025 and then speculated their agricultural "carbon neutral" process under the framework of agricultural carbon compensation rate, so as to summarize the effective paths for different provinces and cities to achieve agricultural "carbon peak and carbon neutral." The results showed that： ① Changes in agricultural carbon emissions in the Yangtze River Economic Zone during the observation period followed an inverted U-shape and peaked in 2015 at 33 312.65×104 tons. The fluctuation of agricultural carbon sinks was relatively small, with an overall upward trend. The upward trend of the agricultural carbon offset rate was obvious, but it still belonged to the "net carbon emission" region. ② Regional differences of agricultural carbon offsetting rate were prominent, and there was a polarization phenomenon, with "net carbon sink" cities significantly less than "net carbon emission" cities. ③ Shanghai, Zhejiang, and Sichuan reached the peak of agricultural carbon emissions in 2006, which Anhui and Chongqing reached in 2012, and the rest of the provinces and municipalities showed a clear upward trend. ④ Anhui, Chongqing, Sichuan, and Yunnan crossed the "agricultural carbon neutral line" and achieved agricultural carbon neutrality. Jiangsu was expected to achieve this in 2026-2030, whereas the remaining provinces and municipalities faced greater difficulties.

Improved nitrogen fertilizer management reduces nitrous oxide emissions in a northern Prairie cropland

Arable croplands are a significant source of nitrous oxide (N2O) emissions, largely due to nitrogen (N) fertilizer applications to support crop production. Nevertheless, there is limited research on the N2O dynamics from canola-wheat rotations in the semi-arid northern Prairies, an important agricultural region. Here, we present micrometeorological N2O fluxes measured from January 2021 to April 2024 in Saskatchewan, Canada, to evaluate the impact of N fertilizer management on the year-round N2O emissions from a canola-wheat rotation. A combination of two 4R (Right Source, Right Rate, Right Time, Right Place) N management practices – a reduced N rate and an enhanced efficiency N fertilizer source – was compared to common fertilizer management practices for the region. Two periods at high risk for N2O flux events were identified, after N fertilizer applications and the following spring thaw, with the magnitude of emissions varying over the multi-year period. As for cumulative emissions, the growing season (GS) N2O emissions were 50 % of annual emissions, presenting an opportunity to mitigate N2O emissions through improved N fertilizer management. Indeed, the improved 4R N management reduced N2O emissions by 57 % over the entire study period without impacting yields. The reduction in GS N2O emissions resulted from the 4R N management lowering mean N2O flux at times of high WFPS (>50 %). The non-growing season (NGS) N2O accounted for 11–67 % of annual emissions. Fall soil nitrate levels were a strong explanatory variable of NGS emissions (r2 = 0.69, r2 = 0.39), but the rate of change and magnitude of NGS emissions depended on thawing conditions – lower for drier thaws, higher for wetter thaws. Ultimately, better N fertilizer management reduces cumulative N2O emissions from cropping systems when practiced for several years.

Engineering a fluorescent probe for the visual and wearable detection of N2H4 in foods, environment samples and biological imaging

Hydrazine (N2H4), as an important chemical raw material widely used in many fields, caused serious harm to human, food and environment. Therefore, a smart fluorescent probe TPC-N2H4 with intramolecular charge transfer (ICT) process was fabricated for colorimetric and fluorescent differentiating of N2H4 in many samples. When TPC-N2H4 met N2H4, blue emission at 451 nm was obviously observed attributed to the interruption of ICT process, resulting in the formation of the hydrazone structure. Additionally, smartphone-assisted colorimetric and fluorescent sensing platforms had been developed for real-time monitoring of N2H4. Importantly, TPC-N2H4 with low toxicity revealed its ability to image N2H4 in HeLa cells, onion and Arabidopsis thaliana. Interestingly, a hydrogel-coated cotton swab sensor was developed with TPC-N2H4 as recognition unit, which showed obvious color and emission changes after N2H4 fumigation with fast response speed. Flexible fluorescent TPC-N2H4-glove was constructed for wearable N2H4 detection and used to monitor N2H4 in food and environment samples. In brief, the TPC-N2H4 probe not only showed great potential in N2H4 detection for environmental and food safety, but also provided a new method for wearable tracking platforms in toxic substance detection.

Health and air pollutant emission impacts of net zero CO2 by 2050 scenarios from the energy modeling forum 37 study

Carbon dioxide and non-greenhouse gas air pollutants are emitted from many of the same sources. Decarbonization actions thus typically yield air pollutant emission reductions, resulting in significant air quality benefits. Although several studies have highlighted this connection, including in the context of net zero carbon emission targets, substantial uncertainty remains regarding how alternative technological pathways to this goal will affect the spatial distribution and magnitude of air pollutants. Comprehensive multi-model and multi-scenario analyzes are needed to explore the relative impacts of alternative pathways. Our study begins to address this gap by leveraging the results from the recent Energy Modeling Forum 37 inter-model comparison exercise on U.S. decarbonization pathways. Comparing the results of the six teams who submitted air pollutant emissions suggests that strategies that target net zero U.S. carbon emissions would yield significant reductions in many air pollutants, and that this finding is generally robust across pathways. However, some energy sources, such as biomass and fossil fuels with carbon capture, will emit air pollutants and can potentially influence the magnitude, spatial distribution, and even sign of localized air pollutant emission changes. In the second part of this analysis, a simplified air quality and health impacts screening model is used to evaluate the air quality impacts in 2035 of sectoral emission changes from the three models that provided sectoral detail. Relative to a reference scenario, a net zero pathway is estimated to reduce fine particulate matter concentrations across the contiguous U.S., with health benefits from reduced mortality ranging from $65 billion to $250 billion in 2035 alone (2023$s). These benefits would be expected to grow over time as the net zero trajectory becomes more stringent. Both the magnitude of potential benefits and the substantial variation of the projections across models underscore the need for an EMF-like inter-model comparison exercise focused on air quality.

Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning

Systematically understanding the impact of meteorological conditions on regional ozone pollution helps to retrieve ozone dataset and evaluate emission changes on ozone variation. Here, more air-quality observation sites were collected, and Random Forest algorithm was applied to retrieve daily maximum 8 h average (MDA8) O3 concentrations at 1 km resolution in 2019-2020 in China. The region-season model and whole-retrieved model were established to compare the contributions of meteorological variables to ozone variations on spatiotemporal scale. The former model outperformed the latter for retrieval capability, but the predictive ability of the latter was slight stronger. This may be associated with the spatiotemporal heterogeneity of meteorological influence. Daily ozone variability in China was mainly influenced by meteorology, especially in North China Plain in autumn. The key factors were temperature, ultraviolet radiation and relative humidity over the whole country, but varied significantly in different regions and seasons. The effects of meteorology and emission sources on ozone were separated by weather normalization technique. Meteorological conditions were particularly favorable for increasing ozone concentrations in spring, but particularly unfavorable in winter. During the COVID-19 lockdown, the increases in ozone in spring and winter of 2020 were the contribution of the combination of meteorology and emissions; while the decreases in ozone in summer and autumn of 2020 were mainly due to the changes of emission sources, although meteorological conditions were unfavorable to ozone mitigation in heavily polluted areas. Our findings provide scientific basis for the prevention and control of ozone pollution for the regional scale in China.