Factors Of Emissions Research Articles

Carbon emissions path of public buildings based on LEAP model in Changsha city (China)

China is currently the world's top carbon emitter due to the acceleration of urbanization, the rapid growth of energy consumption and the rise in carbon emissions, with the construction industry leading the three energy sectors in terms of emission reduction potential. Among the various sectors, the construction industry holds immense potential for emission reduction, with public buildings playing a crucial role. In China, the expansion of public building spaces is closely linked to the pace of urbanization and contributes to a rising share of total building area. Public buildings, characterized by high energy usage, offer a great opportunity for energy conservation and emission reduction. Changsha is experiencing rapid growth and change, and the city is becoming more industrialized and urbanized, which leads to a prominent contradiction between energy supply and demand. There is also great potential for energy conservation and emission reduction in public buildings. Additionally, Changsha is an important node city in the Yangtze River urban agglomeration and the middle reaches of the Yangtze River Economic Belt. It is a typical area that has hot summer and cold winter, thus Changsha is selected as the research case to examine the carbon emission path of public buildings. The primary influencing factors of carbon emissions of public buildings in Changsha City are determined by using the LMDI factor decomposition method addition model. The scenario analysis method is adopted to set the two energy consumption forecast scenarios, which are the baseline scenario and the green scenario. Based on LEAP model and GREAT framework structure, the LEAP prediction model is constructed to predict the carbon emissions of public buildings in Changsha City from 2021 to 2035. The Baseline scenario indicates that while carbon emissions from public buildings in Changsha continue to rise, they are expected to decline starting in 2030. The peak carbon emission of 34.279 million tons of CO2 is predicted for 2032. In contrast, the Green scenario reveals a significantly slower growth rate in carbon emissions from public buildings, with the peak carbon emission projected to reach 27.054 million tons in 2030. This aligns with China's national goal. The findings of this study support the accuracy of the long-term prediction of the model, and offer recommendations for energy conservation and emission reduction in Changsha. This study also provides an example and certain guidance for other provinces and cities that have similar development models and climate conditions. Pertinent development strategies serve as a point of reference for other typical hot summer and cold winter regions.

Characteristics and influencing factors of greenhouse gas emissions from reservoirs in the Yellow River Basin: A Meta-analysis

Characteristics and influencing factors of greenhouse gas emissions from reservoirs in the Yellow River Basin: A Meta-analysis

Regional policy options for carbon peaking in the Yangtze River Delta under uncertainty

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity.

An efficient measure for controlling pollution in the dye industry: Experimental and DFT investigations on direct determination of 3-(N,N-diethylamino)acetanilide

The rapid expansion of the dye industry has led to severe environmental pollution. The lack of real-time direct determination methods for dye intermediates is a key factor in high pollution emissions and low product quality in the continuous synthesis of dyes. This study proposes an accessible, fast, and precise approach for the clean and direct determination of 3-(N,N-Diethylamino)acetanilide (DEAA), a dye intermediate used in the synthesis of the azo dye Disperse Violet 93:1. For precise DEAA quantification, we developed a multi-pathlength and multi-wavelength coupled differential photometric approach based on the absorption spectra characteristics of DEAA at various concentrations and optical pathlengths. A relatively stable analytical sensitivity (0.5671–66.2630) and a large concentration range of 0.01–11% were obtained. Compared to the traditional single-optical pathlength and single-wavelength quantification, this new method possessed 119 times higher sensitivity. The maximum quantitative relative error decreased from 9.17% to 2.44%. The orbital contribution and distinctive peak electronic excitation mechanism of DEAA were uncovered using TD-DFT. By comparing the oscillator strength and peak position variations between the target DEAA and coexisting sulfuric acid, the unique spectral behavior of DEAA, such as its high sensitivity and selectivity, was explained. In addition to providing a reference for real-time industrial dye and intermediate monitoring, this study lays the methodological groundwork for future research. In light of the new method's short time consumption and non-secondary pollution benefits, we further believe it will be crucial in controlling the industrial synthesis process in order to improve product quality, resource conversion efficiency, and effectively prevent excessive pollution generation.

Investigating the driving factors of carbon emissions in China's transportation industry from a structural adjustment perspective

Promoting the transport industry to achieve a carbon peak and controlling carbon emissions in various regions of China through structural adjustment are important means to significantly reduce CO2 emissions. To explore the drivers of carbon emissions from China's transportation sector from 2006 to 2021. And constructs the IPAT-S (IPAT model considering structural adjustment) model, the structural equation is combined to explore the carbon emission reduction path of China's transportation industry from the perspective of structural adjustment. The results show that the structural adjustment is beneficial to alleviate the pressure of emission reduction, carbon emissions from the national transportation sector show a pattern of “low in the north and high in the south”, with the trend gradually shifting to the central region, and the spatial structural effect on carbon emissions has been significantly strengthened, the industrial structure, energy structure and transportation structure still contribute to the carbon emissions of the transportation industry. From the regional level, there are some differences in the impact of structural adjustment on carbon emission reduction in different regions, but optimizing the energy structure will be conducive to carbon emission reduction. In the case of the Northeast region, the energy consumption structure, industrial structure, and transportation structure do not inhibit carbon emissions, and the structural emission reduction in the Central region has begun to bear fruit. The structural adjustment in the eastern and western regions has yet to be improved, which will also provide the government with policy recommendations for structural emission reduction based on regional differentiation.

Estimation of ammonia emissions from rice fields under different N inputs in China

Rice production is essential for ensuring food security. Nitrogen fertilizers applied in rice fields are prone to loss through NH3 emissions. We need to elucidate the responses of NH3 emissions to different N inputs. With a national dataset, we identified that N application rate, N application frequency, soil temperature, and soil silt fraction were the key driving factors of NH3 emissions. 190−210 kg N ha−1 was suitable for simultaneously reducing NH3 emissions and increasing rice yields. The model showed that under the actual N application rate and N application frequency of 3, national NH3 emissions were 0.852 Mt N each rice growing season, while national NH3 emission factor was 12.7 %. We developed a portable software to conveniently assess China's NH3 emissions from rice fields under different N inputs. The findings can help to optimize N inputs in rice fields to reduce NH3 emissions and improve ecosystem health and agricultural sustainability.

Toward sustainable development: Exploring the relationship between economic fitness and carbon emissions in BRICS

Global warming and energy security are currently the two most challenging issues facing the globe. Economic fitness (EF) is a country's ability to deal with complex issues like environmental degradation (ED) and energy insecurity, which are major global concerns. The significance of this study lies in the fact that it investigates the influencing factors of CO2 emissions (CO2E), as these factors contribute to SDGs. This study, which uses panel data from 1995 to 2015, aims to investigate the impact of EF on CO2E from biofuels (BFs) and fossil fuels (FFs) combustion in the BRICS countries. The FGLS and PCSE results show an N-shaped relationship between EF and BFs-CO2E, and an inverted N-shaped association between EF and FFs-CO2E. The results of this study are robust even after including control variables. In addition, urbanization, economic growth, and financial development are positively related to both BFs' & FFs' CO2E. The policy implication is that government should focus on increasing country EF to reduce CO2E. Moreover, this study provides crucial information for the policy makers and practitioners looking for the pathway to reduce CO2E and achieve sustainable development.

Decomposition, decoupling and dynamic prediction of carbon emissions from city-level building operations: a case study of the Yangtze River Delta

Reducing carbon emissions in the building sector is crucial to achieving carbon peak and carbon neutrality goals. Identifying the influencing factors of carbon emissions from building operations at the city level and predicting carbon peaks is of great significance for developing locally adapted policies. This article examines the spatial and temporal trends of carbon emissions at the building operation stage in the Yangtze River Delta (YRD) region over the last 15 years. This article analyzes the driving factors at a city level using the GDIM method and evaluates the decoupling status of income growth and carbon emissions with the GDIM-based decoupling model. The peak time and peak value of operational carbon emissions in the YRD region are predicted through a combination of scenario analysis and the Monte Carlo method. The research shows that: (1) the total carbon emissions from the building operational stage in the YRD region increased by 163.63%, of residents’ disposable income and energy consumption contributed the most. (2) In terms of decoupling relationship between carbon emissions and income growth, rural residential buildings (RR) outperform urban residential buildings (UR) and public buildings (PB). (3) According to the dynamic simulation, the operational carbon emission in the YRD region is expected to peak at 498.18–504.83 Mt MtCO2 between 2030 and 2035. (4) The peak of PBs and URs in Nanjing, Suzhou and some third- and fourth-tier cities, and RRs in eastern and central cities in the YRD will occur at the latest, necessitating close monitoring. These findings can serve as a basis for developing scientific and reasonable building emission reduction policies in a multilevel governance context.

Analysis of Influencing Factors and Prediction of the Peak Value of Industrial Carbon Emission in the Sichuan-Chongqing Region

The greenhouse effect has a negative impact on social and economic development. Analyzing the factors influencing industrial carbon emissions and accurately predicting the peak of industrial carbon emissions to achieve carbon peak and carbon neutrality is therefore vital. The annual data from 2000 to 2022 were used to study the influencing factors of carbon emission and the path of carbon emission reduction. In this study, the gray correlation method and stepwise regression method were used to explore the effective factors that met the significance test and the STIRPAT expansion model was constructed to analyze the characteristics and influencing factors of industrial carbon emissions in the Sichuan-Chongqing region. Finally, the changing trend of regional industrial carbon emissions is predicted by scenario analysis and four development scenarios are set up, which show that (1) from 2000 to 2022, the change in total industrial carbon emissions in Sichuan Province and Chongqing Municipality presents an inverted U-shaped trend, reaching a phased peak in 2013 and 2014, respectively, then declining and then rising again after 2018. (2) Industrial scale foreign trade dependence and population are the effective factors of industrial carbon emission in Sichuan, and all have positive effects. Energy structure and per capita income are the effective factors in Chongqing, showing negative and positive effects, respectively. (3) Analysis of four scenarios shows that the time range of the industrial carbon peak in the Sichuan-Chongqing region is 2030–2035 and that its peak height ranges from 81.98 million tons to 87.64 million tons. Among them, the green development scenario is the most consistent path to achieve the carbon peak as soon as possible; in this case, industrial carbon emissions will peak in 2030, in line with the national target time, and the lowest peak level of 81.98 million tons. The suggestions in this paper are continuously optimizing the energy structure, adjusting the industrial scale, and accelerating scientific and technological progress to achieve sustainable development.

Analysis of the spatial–temporal evolution and driving factors of carbon emission efficiency in the Yangtze River economic Belt

As an important economic growth pole and ecological area in China, the urban agglomeration of the Yangtze River Economic Belt (YREB) is the key to carbon emission reduction. Exploring the spatial–temporal evolution and driving variables of its carbon emission efficiency (CEE) is crucial for realizing the goals of carbon peaking and carbon neutrality. The super-efficiency SBM model, the nuclear density method, and the spatial autocorrelation method were used to discuss the spatial–temporal CEE characteristics of 105 cities in the YREB. On the driving factors of carbon emissions, the geographic detector and the Tobit model were combined to explore the spatial differentiation characteristics of the driving factors from the perspective of heterogeneity, and concurrently analyze the single-factor’s effecting intensity and impacting direction, as well as the dual-factors’ interaction effects. The findings indicated that the CEE of YREB generally showed a slow upward trend during 2006–2021. From the perspective of time dynamic evolution, the differentiation of efficiency intensified, and the overall development was toward the high efficiency level. Furthermore, the results of spatial pattern evolution showed that the CEE presents the pattern of “downstream areas > midstream areas > upstream areas”, “high in the east and low in the west”, “hot in the east and cold in the west”, while the spatial clustering effect was significant, showing the distributions of low-low clustering or high-high clustering. Moreover, the results of the geographic detector showed that government intervention, economic growth, and technological progress were the main driving factors. In addition, the interactions government intervention and the other factors were significantly detected. Tobit regression results showed that technological advancement and economic growth had a favorable impact on CEE, but foreign investment, urbanization, and government involvement had negative impacts. In the future, the correlations between provinces and cities should be strengthened and amplified to promote the integrated green development, as well as to improve the ecological environments.

The role of renewable energy and total factor productivity in reducing carbon emissions: A case of top-ranked nations in the renewable energy country attractiveness index

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results.20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates.The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent.

Carbon Emission Measurement Method of Regional Power System Based on LSTM-Attention Model

With the acceleration of the green and low-carbon transformation of the power system, it is very important to calculate and analyze the carbon emissions of the urban power system. In order to effectively grasp the carbon emission distribution of power system and reduce the carbon emission of power system, this paper proposes a systematic carbon emission measurement method for regional power system. Firstly, the quantitative analysis model of driving factors for regional power system carbon emissions is constructed, and the direction and measures of low-carbon transformation and green collaborative development of regional power system are proposed. Secondly, energy consumption scenarios under different constraints are established to support the collaborative control path of CO2. It provides key data and a theoretical basis for the low-carbon development of the power industry. Finally, a systematic carbon emission measurement method based on LSTM-Attention is proposed. Finally, the effectiveness of the proposed method is verified by an example analysis, and the effective path of carbon emission reduction in the future is analyzed.

A new IPCA-CPSO-BP Model for Predicting Gas Emission in Underground Coal Mines

Gas has become the primary factor restricting the safe and efficient production of coal mines. Gas emission prediction model is very important for mine gas emission prediction and gas disaster prevention. The improved principal component analysis (IPCA) was used to reduce the dimension of 13 influencing factors of gas emission, and the CPSO-BP neural network prediction model was built with MATLAB software to predict the absolute gas emission of Zhongtai Mining.The results show that the cumulative contribution rate of principal components is not less than 95%, and the number of principal components after improvement is reduced from the original 6 to 3, which effectively solves the problem of excessive principal components and data redundancy caused by the correlation difference between various influencing factors and gas emission amount. At the same time, the optimized particle swarm optimization algorithm alleviates the trouble that the particle swarm optimization algorithm is prone to fall into the local optimal solution. By coupling the improved particle swarm optimization algorithm with BP neural network, the problem of BP neural network's over-dependence on the initial value of weights and thresholds is solved, and the optimal initial weights and thresholds are provided for BP neural network, improving the accuracy of model prediction results. The average relative error of the original BP neural network is 1.6638%, the normalized mean square error is 0.3155, and the regression correlation index is 0.8157. The IPCA-CPSO-BP prediction model decreased to 0.5749%, 0.1143 and 0.9758, respectively. IPCA improves the data dimensionality reduction ability of principal component analysis, IPCA-CPSO-BP enhances the stability of particle swarm optimization algorithm, and significantly improves the prediction accuracy of BP model. The prediction trend is highly consistent with the actual value, which verifies the reliability of the model and provides strong data support for mine gas prevention and control.

Decision tree-based approach to extrapolate life cycle inventory data of manufacturing processes

Life cycle assessment (LCA) plays a crucial role in green manufacturing to uncover the critical aspects for alleviating the environmental burdens due to manufacturing processes. However, the scarcity of life cycle inventory (LCI) data for the manufacturing processes is a considerable challenge. This paper proposes a novel approach to extrapolate LCI data of manufacturing processes. Taking advantage of LCI data in the Ecoinvent datasets, decision tree-based supervised machine learning models, namely decision tree, random forest, gradient boosting, and adaptive boosting, have been developed to extrapolate the data of GHG emissions, i.e., carbon dioxide, nitrous oxide, methane, and water vapor. Initially, a correlation analysis was conducted to derive the most influential factors on GHG quantities resulting from manufacturing activities. First, the collected data have been preprocessed and split into train and test sets (70% and 30%, respectively). Second, a five-fold cross-validation method was applied to tune the hyperparameters of the models. Then, the models were re-trained using the best hyperparameters and evaluated using the test set. The results reveal that the Gradient Boosting model has a superior predictive performance for extrapolating the GHG emission data, with average coefficients of determination (R2) on the test set <0.95. Moreover, the model predictions involve relatively low values of the average root mean squared error and an average mean percentage of error on the test set. The correlation and feature importance analyses emphasized that the workpiece material and manufacturing technology have a considerable effect on natural resource consumption, i.e., energy, material, and water inflows into the process. Meanwhile, energy consumption, water usage, and raw aluminum depletion were the most influential factors in GHG emissions. Eventually, a case study to extrapolate the inflows and the outflows for new manufacturing activities has been conducted using the validated models. The proposed GraBoost model provides a computational supplementary approach to estimate and extrapolate the GHG emissions for different manufacturing processes when LCI data are incomplete or don't exist within LCI databases.

Spatiotemporal dynamic evolution and influencing factors of land use carbon emissions: evidence from Jiangsu Province, China

Land use/cover change has an important impact on global climate change and carbon cycle, and it has become another major source of carbon emission after energy consumption. Therefore, this study focuses on the main line of “land use carbon emissions-spatial and temporal patterns-influencing factors,” and selects 13 cities in Jiangsu Province as the research object. Based on the data of land use and energy consumption, combined with the method of land use carbon emissions and ArcGIS technology, this study conducted a quantitative analysis of the spatio-temporal distribution of carbon emissions in Jiangsu Province. The factors affecting the spatial distribution of carbon emissions from land use in Jiangsu Province were discussed by using Geographic detector. The results show that: 1) Carbon emissions in Jiangsu Province showed an overall growth trend, from 16215.44 ×104tC in 2010–23597.68 ×104tC in 2020, with an average annual growth rate of 4.55%, of which the construction land and watersheds had a greater impact on carbon sources and sinks, respectively. 2) During the period, there were significant differences in carbon emission levels among different cities in Jiangsu Province, and the land use carbon emission in Jiangsu Province showed a stable spatial pattern of “northwest—southeast.” The southern part of Jiangsu is always the hot area of carbon emission, while the cold spot area is mainly distributed in the northern and central parts of Jiangsu. 3) The interaction of factors such as economic development, industrial structure, energy intensity, land use and human activities is an important reason for the spatio-temporal differences of land use carbon emissions in Jiangsu Province. Among them, the level of urbanization, population size and economic aggregate have significant effects on carbon emissions.

Differential Analysis of Carbon Emissions between Growing and Shrinking Cities: A Case of Three Northeastern Provinces in China

Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China’s three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies.

Characteristics and influencing factors of CO2 emission from inland waters in China

Characteristics and influencing factors of CO2 emission from inland waters in China

Spatiotemporal variations and influencing factors of methane emissions from livestock in China: A spatial econometric analysis

In recent years, China has been implementing policies to improve the livestock industry in response to the global trend toward green and low-carbon development. These policies include the establishment of demonstration zones for high-standard agriculture, the relocation of farms to the north, etc. This study aims to investigate the impact of changes in the spatial structure of the livestock industry on methane emissions. It used panel data from 31 provinces in China from 2001 to 2021 and applied the IPCC methodology to quantify methane emissions at both the national and provincial levels. In addition, a spatial econometric model was used to analyze the impact of changes in the spatial structure of the livestock industry on methane emissions. The results show that methane from livestock in China decreased from 13.85 million tons in 2001 to 11.82 million tons in 2021. In addition, methane emissions from livestock in China show a significant spatial gradient and correlation. The Southwest has the highest methane emissions, accounting for 24 % of the total emissions. After controlling for spatial correlation and other factors in the model, it was found that the spatial structure of the livestock industry has a different influence on methane emissions both in the province and in neighboring provinces. To improve methane emission efficiency in the future, policies such as establishing functional zones for livestock farming, strengthening technological innovation and sharing for green development in agriculture, and promoting the optimization of agricultural and rural management structures should be implemented.

Pathways to mitigate household carbon emissions in Chinese cities: Intentions, actions, and environments

Cities represent primary origins of carbon emissions and key spaces to cope with climate change. Low-carbon city pilots (LCP) have carried out three groups to mitigate household carbon emissions in China. There are many difficulties in identifying possible sources and factors of household carbon emissions. This study attempted to construct the analytical framework to examine potential mitigation pathways, which would influence household carbon emissions. Collecting data from urban energy consumption, the difference-in-differences model, a widely utilized quantitative method, is employed to assess the policy effect of LCP on mitigating household carbon emissions in China. Results indicated that: (1) LCP could mitigate household carbon emissions (−7.6%) and per capita household carbon emissions (−8.8%), which passed serious robustness tests. (2) Potential mechanisms, through which LCP mitigate household carbon emissions, were recognized, in terms of subjective intentions, objective actions, and external environments. (3) Policy effects in eastern and central cities were significantly negative.The improvement of energy consumption for winter heating in northern cities could mitigate more household carbon emissions.

The Impact of Agroecosystems on Nitrous Acid (HONO) Emissions during Spring and Autumn in the North China Plain.

Solar radiation triggers atmospheric nitrous acid (HONO) photolysis, producing OH radicals, thereby accelerating photochemical reactions, leading to severe secondary pollution formation. Missing daytime sources were detected in the extensive HONO budget studies carried out in the past. In the rural North China Plain, some studies attributed those to soil emissions and more recent studies to dew evaporation. To investigate the contributions of these two processes to HONO temporal variations and unknown production rates in rural areas, HONO and related field observations obtained at the Gucheng Agricultural and Ecological Meteorological Station during spring and autumn were thoroughly analyzed. Morning peaks in HONO frequently occurred simultaneously with those of ammonia (NH3) and water vapor both during spring and autumn, which were mostly caused by dew and guttation water evaporation. In spring, the unknown HONO production rate revealed pronounced afternoon peaks exceeding those in the morning. In autumn, however, the afternoon peak was barely detectable compared to the morning peak. The unknown afternoon HONO production rates were attributed to soil emissions due to their good relationship to soil temperatures, while NH3 soil emissions were not as distinctive as dew emissions. Overall, the relative daytime contribution of dew emissions was higher during autumn, while soil emissions dominated during spring. Nevertheless, dew emission remained the most dominant contributor to morning time HONO emissions in both seasons, thus being responsible for the initiation of daytime OH radical formation and activation of photochemical reactions, while soil emissions further maintained HONO and associated OH radial formation rates at a high level, especially during spring. Future studies need to thoroughly investigate the influencing factors of dew and soil emissions and establish their relationship to HONO emission rates, form reasonable parameterizations for regional and global models, and improve current underestimations in modeled atmospheric oxidation capacity.