Amount Of Emissions Research Articles

Correlation Methodologies between Land Use and Greenhouse Gas emissions: The Case of Pavia Province (Italy)

The authors present an analysis of the correlation between demographic and territorial indicators and greenhouse gas (GHG) emissions, emphasizing the spatial aspect using statistical methods. Particular attention is given to the application of correlation techniques, considering the spatial correlation between the involved variables, such as demographic, territorial, and environmental indicators. The demographic data include factors such as population, demographic distribution, and population density; territorial indicators include land use, particularly settlements, and road soil occupancy. The aims of this study are as follows: (1) to identify the direct relationships between these variables and emissions; (2) to evaluate the spatial dependence between geographical entities; and (3) to contribute to generating a deeper understanding of the phenomena under examination. Using spatial autocorrelation analysis, our study aims to provide a comprehensive framework of the territorial dynamics that influence the quantity of emissions. This approach can contribute to formulating more targeted environmental policies, considering the spatial nuances that characterize the relationships between demographics, territory, and GHGs. The outcome of this research is the identification of a direct formula to obtain greenhouse gas emissions from data about land use starting from the case study of Pavia Province in Italy. In the paper, the authors highlight different methodologies to compare land use and GHG emissions to select the most feasible correlation formula. The proposed procedure has been tested and can be used to promote awareness of the spatial dimension in the analysis of complex interactions between anthropogenic factors and environmental impacts.

Carbon Footprint of the Food Industry and ESG-Related Business Promotion

The carbon emissions of the food industry account for a significant proportion of the overall carbon emissions. Figuring out the carbon footprint of the food industry is a great way to find out the reason for the large amount of carbon emissions. In this part, the paper selected several variations that may have some influence on carbon emissions and did some intensive studies to figure out which one is the major factor. By analyzing through life cycle assessment and regression by Random Forest, it was figured that fertilizer is the most influential factor in carbon emissions, and focusing more on the usage of compound fertilizer and decreasing the usage of other kinds of fertilizer can effectively reduce the carbon emissions. Then the paper tried to figure out whether the reduction is realistic, and the paper did research in the financial field. The paper did an ESG score ranking of the fertilizer company, cultivated the investment weight in the portfolio, and did a risk rating of them. The research adopted the Grey Forecasting Model and quadratic Optimization Formulation to process data. Through the simple study of the carbon footprint of fertilizers and an analysis of the investment ratio of investors, it is finally found that to economically promote and support the development of carbon-reducing compound fertilizers by business is essential. Besides, it coincides with investors’ interests.

The Impact of Vehicle Technology, Size Class, and Driving Style on the GHG and Pollutant Emissions of Passenger Cars

Although technical improvements to engines and aftertreatment systems have the greatest impact on pollutant emissions, there is also potential for reducing emissions through driver behavior. This potential can be realized in the very short term, while better emission-control technologies only take effect once they have penetrated the market. In addition to a change in driving style, the vehicle owner’s choice of vehicle technology and size class will also have an impact on the future emissions of the vehicle fleet. The effects of different driving styles, the tire choice, the vehicle size class, and propulsion technologies on energy consumption and tailpipe and non-exhaust emissions are analyzed in this paper for different traffic situations and start temperatures for cars with petrol and diesel combustion engines and for battery electric vehicles. The analysis is completed with the corresponding upstream emissions from fuel and electricity production. The analysis is based on a vehicle simulation using the Passenger car and Heavy-duty Emission Model (PHEM), which is based on a large database of vehicles created using measurements of real driving conditions. For the assessment of the driving style, a novel method was developed in an H2020 project, which reproduces a measured trip with a virtual eco-driver. Carbon dioxide equivalent emissions (CO2eq) increase with increasing vehicle size, but can be reduced by around 20% for conventional vehicles and 17% for battery electric vehicles (BEVs) through an environmentally conscious driving style. On average, BEVs have around 50% lower CO2eq emissions than conventional vehicles, if the emissions from vehicle production are also taken into account. On an average journey of 35 km, the cold start of modern diesel vehicles accounts for around half of the total NOx emissions, while the proportion of cold starts for petrol vehicles is around 25%. Tire and brake wear together generate a similar amount of PN23 emissions as the exhaust gases from new cars.

Spatial-Temporal Evolution and Influencing Factors of Animal Husbandry Carbon Emissions: A Case Study of Shandong Province, China

To further study the spatial distribution and dynamic evolution of carbon emissions from animal husbandry in Shandong Province, the panel data of 16 prefecture-level cities in Shandong Province from 2001 to 2022 were used to measure the carbon emissions of animal husbandry and the carbon emission intensity of animal husbandry. Based on the combination of space, kernel density estimation, and LMDI decomposition model, the spatial and temporal evolution of carbon emissions from animal husbandry in Shandong Province and its driving factors were investigated. The results show that: (1) The total amount of animal husbandry carbon emissions in Shandong Province showed a fluctuating downward trend, with a decrease of 10.10% during the investigation period, showing a peripheral-agglomeration distribution pattern. The carbon emission intensity showed a gradual downward trend, with an average annual decline of 7.47%, showing stepped distribution characteristics of high in the west and low in the east. (2) The difference in carbon emissions of animal husbandry among cities in Shandong Province increased first and then decreased, and the growth distribution was basically in the form of “bimodal”, showing a polarization pattern. (3) The intensity effect has the most obvious inhibitory effect on the carbon emission of animal husbandry; the effect of agricultural structure changes from a promoting effect to an inhibiting effect. The inhibitory effect of the industrial structure effect is second only to the intensity effect; the economic effect has the greatest promoting effect; and the promotion effect of the population size effect is small.

The Influence of Cropping Systems and Tillage Intensity on Soil CO2 Exchange Rate

In order to control the amount of greenhouse gas emissions from agriculture, it is necessary to select the appropriate crop rotation and tillage intensity depending on the soil type and climatic conditions. However, their implementation in agricultural management methods has been insufficient until now. The main objective of this study was to investigate the changes and dependences in soil net CO2 exchange rate (NCER) and main physico-chemical parameters under different tillage (conventional (CT) and no tillage (NT)) and crop rotation systems. Cropping systems significantly affected the amount of nutrients, but did not affect pH and organic carbon; otherwise, tillage vs. cropping systems had no significant effects on the soil chemical parameters analyzed. The data revealed that in NT treatments, the NCER was 28% higher than in CT. Different crop rotations also revealed a significant effect on NCER from the soil. Carbon dioxide fluxes increased in cropping systems where a higher share of catch crops were included. In NT systems, a comparatively higher soil moisture content was registered. In addition, the rotations with catch crops produced a higher (by 1–3%) soil moisture content. The temperature of the soil surface was not significantly affected by tillage or cropping systems; nevertheless, a trend towards higher soil surface temperatures in CT was determined, which might be affected by enhanced air circulation in the pores. Soil NCER increase correlated negatively with higher soil surface moisture content, while its relationship was positive with soil surface temperature increase. In general, soil surface temperature and moisture were the most significant factors in explaining the fluctuation in NCER from Cambisols in Lithuania under moderate climatic conditions.

Investigation of emissions from passenger flights Denizli Çardak Airport, Türkiye

AbstractDue to developing aviation sector, number of aircraft in the world is increasing. Along with this development, problems such as the decrease in air quality in and around the airport also arise. In this study, it is tried to calculate pollutant emissions occurring in 2022 during the LTO cycles of Denizli Çardak Airport in Turkey. These calculations are based on the information obtained from ICAO Engine Emission Data Bank and flight information published by the General Directorate of State Airports Authority (GDSAA). As a result of the data obtained, 74.64 ton/year pollutants (NOx-37.148 t/y, CO-35.398 t/y and HC-2.094 t/y) were calculated for 2022 at Denizli Çardak Airport. Of all emissions, NOx accounted for 50%, CO 47% and HC 3%. In the LTO cycle, the most fuel is burned in taxi cycle and pollutant emissions produced in this cycle are greater. With a 2 min reduction in taxi time, there will be an approximate 6.8% reduction in the total emission rate in the LTO cycle. Similarly, with a 4 min reduction in taxi time, there will be a 13.72% reduction in the whole emission rate in the LTO cycle. Unlike other studies, in this study the emission rates of various engines were compared. It has been calculated that the amount of pollutant emissions produced by the new generation Boeing 737 MAX LEAP-1B powered aircraft in LTO cycle is 25% less than the amount of pollutant emissions produced by the Airbus A320 NEO LEAP-1 A powered aircraft. The biggest factor here is that the emission of CO pollutants is less. Considering the emission rates produced by these four different engines (B737-800 CFM56-7B, A320 V2500-A1, B737 MAX LEAP-1B, A320 NEO LEAP-1 A), the Airbus A320 V2500-A1 engine is a more environmentally friendly engine than the other engines.

Underestimated contribution of open biomass burning to terpenoid emissions revealed by a novel hourly dynamic inventory

Terpenoids play a crucial role in atmospheric chemistry, contributing significantly to the formation of ozone and secondary organic aerosol. However, the accurate quantification of terpenoid emissions from biomass burning is currently lacking, leading to underestimated air quality impacts. This study developed a near real-time hourly open biomass burning (OBB) emission inventory named OBEIC, which incorporated geostationary and polar-orbiting satellite fire radiative power. The OBEIC inventory provided emission estimates of 69 terpenoids, categorized into four groups, at an hourly resolution. Monoterpenes were the dominant contributors to the total emissions, accounting for 58 % of the total terpenoid emissions from OBB. Notably, only 24 % of the total monoterpenes emitted from OBB were accounted for by α-pinene and β-pinene, indicating the importance of quantifying emissions of other monoterpene species such as limonene and camphene. Additionally, oxygenated terpenoids, which were previously overlooked, contribute to 20 % of total terpenoid emissions from OBB. Diurnally, the emissions of terpenoids were primarily concentrated during the daytime (61 %); however, this study revealed the significance of nighttime emissions (39 %) as well. When compared to the biogenic and anthropogenic emissions, OBB made substantial contributions to nighttime isoprene (99.8 %), monoterpene (66.8 %), and sesquiterpene (61.7 %) emissions where OBB occurs (in 3 km range), suggesting its significant role in nighttime secondary pollutant formation. The methodology developed in this study has the potential to reduce uncertainties in OBB emissions estimation.

Conventional activated sludge vs. photo-sequencing batch reactor for enhanced nitrogen removal in municipal wastewater: Microalgal-bacterial consortium and pathogenic load insights

Municipal wastewater treatment plants are mostly based on traditional activated sludge (AS) processes. These systems are characterised by major drawbacks: high energy consumption, large amount of excess sludge and high greenhouse gases emissions. Treatment through microalgal-bacterial consortia (MBC) is an alternative and promising solution thanks to lower energy consumption and emissions, biomass production and water sanitation. Here, microbial difference between a traditional anaerobic sludge (AS) and a consortium-based system (photo-sequencing batch reactor (PSBR)) with the same wastewater inlet were characterised through shotgun metagenomics. Stable nitrification was achieved in the PSBR ensuring ammonium removal > 95 % and significant total nitrogen removal thanks to larger flocs enhancing denitrification. The new system showed enhanced pathogen removal, a higher abundance of photosynthetic and denitrifying microorganisms with a reduced emissions potential identifying this novel PSBR as an effective alternative to AS.

Estimating Greenhouse Gas Emissions from Household Activities: A Case in Bogor Indonesia

Climate change is a result of global warming. Global warming is caused by an increase in the concentration of greenhouse gases (GHGs) in the atmosphere. One of the largest GHG-contributing sectors is the energy sector, which includes households with various activities. Household activities include using LPG, electricity, gasoline, waste generation, and farming and livestock activities to meet the household's needs. The level of GHG emissions produced in households is influenced by income, years of study, and knowledge of emissions. The objectives of this study are to estimate the amount of GHG emissions generated from household activities, analyze the factors that influence GHG emissions, and estimate the value of the carbon economy. The analytical methods used are quantitative descriptive analysis, IPCC (2006) method, multiple linear regression, and economic value analysis. The results showed that: (1) CO2-eq emissions generated in Sinarsari Village for 100 respondents amounted to 32,011.68 kg CO2-eq/month, and the amount of GHG emissions in one village scope amounted to 853,431.36 kg CO2-eq/month. (2) Income, length of study, and emission knowledge dummy influence GHG emissions. 3) Carbon economic value based on carbon pricing is Rp287,415,199.70/month.

Integrated-path multi-gas sensor using near-infrared diode lasers: An alternative to vehicle-driven point gas analyzer

Sensors for quantitative monitoring of greenhouse gases from high-emission facilities, such as wastewater treatment and industrial plants, are becoming essential to enforce regulatory compliance in climate action initiatives. A predominant sensing technique is the tracer-gas dispersion method (TDM) using closed-path gas analyzers typically installed in a vehicle to measure concentration as the gas plume is transected. Here, we validate for the first time the use of integrated-path gas sensors in TDM, measuring instantaneously the concentration along a predetermined laser beam path, thereby facilitating a stationary setup with orders of magnitude higher update rate and without the need for vehicles or road access. Our approach relies on a sensitive, integrated-path multi-gas sensor for real-time emission quantification using tunable diode laser absorption spectroscopy. An optical sensor head with spectral coverage from 1.52 μm to 1.65 μm is demonstrated, supporting multiplexed gas sensing in a modular fashion using low-power fiber-coupled diode lasers. The sensor measures real-time integrated-path concentrations of CO2 and CH4, and the tracer-gas C2H2 at 130 Hz with 0.5 ppm, 3 ppb, and 2 ppb sensitivity, respectively, at 1 s averaging. The integrated-path sensor is systematically benchmarked for absolute accuracy against a state-of-the-art point sensor, revealing an excellent match over 16 hours. Furthermore, the integrated-path sensor is deployed outdoors for emission quantification using TDM. A comparison to the gold-standard point sensor reveals identical results, demonstrating the integrated-path sensor as a potent substitute for vehicle-driven point sensors.

More applicable quantification of non-CO2 greenhouse gas emissions from wastewater treatment plants by on-site plant-integrated measurements

Wastewater treatment is an important source of non-CO2 greenhouse gases (GHGs). However, current quantification of these GHG emissions mainly employs unit-based measurements, where emissions from individual process units are identified, leading to large uncertainties of overall emissions. Here we introduce plant-integrated measurements, where emissions from the whole plant are measured through the off-gas pipelines of the enclosed facility, to quantify methane (CH4) and nitrous oxide (N2O) emissions from an underground municipal wastewater treatment plant (WWTP) in southern China. Our results show that the primary oxic tank contributes the largest in total CH4 and N2O emissions, with an average fraction of over 80 % and over 90 %, respectively. This can be attributed to the vigorous aeration process, which facilitates the transfer of dissolved CH4 and N2O from the liquid phase to the atmosphere through intensive air stripping. The plant-integrated measurements yield around 3–9 times higher emission factors of CH4 and N2O than the unit-based measurements. This difference in emission accounting is attributed to both varying survey durations of the two approaches and the omission of uncertain emission sources during unit-based measurements. The comparison between these two approaches indicates that plant-integrated measurements are more applicable for emission quantification of the whole plant whereas unit-based measurements provide insights into the emission characteristics of individual process units. More plant-integrated measurements are needed in the future for more accurate emission accounting of WWTPs.

CarbonKG: Industrial Carbon Emission Knowledge Graph-Based Modeling and Application for Carbon Traceability of Complex Manufacturing Process

Abstract In the context of the 2030 carbon dioxide emissions peak target, achieving carbon neutrality in manufacturing is essential. However, the complex and extensive supply chain, ranging from raw materials to the final product, presents significant challenges in assessing the carbon footprint throughout the lifecycle. This paper concentrates on the manufacturing process and introduces a modeling approach for quantifying and predicting carbon emissions, utilizing industrial intelligent technologies like process mining and knowledge graphs. First, it is crucial to establish a comprehensive carbon emission quantification model for each manufacturing stage, encompassing the energy, material, personnel, and carbon flow. Subsequently, this paper proposes an industrial carbon emission knowledge graph-based model (CarbonKG) to record and compute emissions at each production stage. Furthermore, process mining technology aids in analyzing the global distribution and movement of carbon emissions within the manufacturing process. Finally, this research presents a two-stage predictive approach for manufacturing process carbon emissions based on CarbonKG. The first stage involves initializing a local order carbon graph for prediction and developing a model to find similar cases. The second stage uses a graph-matching model to identify the Top-K similar order cases, using their carbon emission data for comprehensive prediction. Moreover, the feasibility of the proposed method was verified using actual production process data from industrial companies.

Greenhouse gas and ammonia emissions in modelled cereal crop production under Polish agricultural conditions: An example spring barley

Based on the analysis of statistical data, the average area under cultivation and average yields of barley in Poland in 2010–2020 were calculated. Barley is one of the most important cereals grown in Poland. Its cultivation area occupied an average of 920,595 ha in these years, with average yields of 3.66 Mg∙ha−1. Barley is a cereal grown mainly as a spring cereal. The average area under spring species in these years accounted for 95% (875,771 ha) of the total area under this cereal, and the average yield of spring varieties was 3.60 Mg∙ha−1. In order to estimate emissions of greenhouse gases (GHG) and ammonia (NH3), the sources of emissions – inorganic fertilisers, fuel consumption – were analysed for selected barley cultivation technologies, differentiated by yield level, and variant model production technologies were developed to obtain projected yields. Emissions were calculated for individual greenhouse gases (N2O, CH4 and CO2) and they were recalculated according to the greenhouse potential of each gas (GWP – global warming potential) to be able to compare the total amount of greenhouse gas emissions for the analysed variants. Greenhouse gas emissions for cultivation technology ranged from 134.53 to 136.48 kg CO2eq for 1 Mg yield. A more accurate Tier 2 method was used to estimate NH3 emissions, taking into account soil conditions and climate zone. The estimated ammonia emissions from the application of mineral fertilisers were from 0.99 kg to 2.35 kg for 1 Mg yield.

Carbon Monitor Power-Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power generation

Abstract. The impact of climate change on power demand and power generation has become increasingly significant. Changes in temperature, relative humidity, and other climate variables affect cooling and heating demand for households and industries and, therefore, power generation. Accurately predicting power generation is crucial for energy system planning and management. It is also crucial to understand the evolution of power generation to estimate the amount of CO2 emissions released into the atmosphere, allowing stakeholders to make informed plans to reduce emissions and to adapt to the impacts of climate change. Artificial intelligence techniques have been used to investigate energy-demand-side responses to external factors at various scales in recent years. However, few have explored the impact of climate and weather variability on power demand. This study proposes a data-driven approach to model daily power demand provided by the Carbon Monitor Power project by combining climate variables and human activity indices as predictive features. Our investigation spans the years 2020 to 2022 and focuses on eight countries or groups of countries selected to represent different climates and economies, accounting for over 70 % of global power consumption. These countries include Australia, Brazil, China, the European Union (EU), India, Russia, South Africa, and the United States. We assessed various machine-learning regressors to simulate daily power demand at the national scale. For countries within the EU, we extended the analysis to one group of countries. We evaluated the models based on key evaluating metrics: coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and median absolute error (MedAE). We also used the models to identify the most influential variables that impact power demand and determine their relationship with it. Our findings provide insight into variations in important predictive features among countries, along with the role played by distinct climate variables and indicators of the level of economic activity, such as weekends and working days, vacations and holidays, and the influence of COVID-19.

Carbon Storage and Sequestration Analysis by Urban Park Grid Using i-Tree Eco and Drone-Based Modeling

Urban areas play a crucial role in carbon absorption, while also producing a considerable amount of carbon emissions. However, there has been a lack of research that has systematically examined the carbon storage and sequestration in green spaces located within urban environments, at a spatial scale. This study analyzes carbon storage and sequestration in Yurim Park, Daejeon, South Korea on a grid basis to fill the research gap. The research compares the variation in sequestration capacity across different grids and provides insights into the development of sustainable urban parks in urban planning. The classification of grids is based on specific site characteristics, such as land cover, tree distribution, type, and density. This results in a total of seven distinct types. The study employs a combination of the I-tree eco model, drone-based modeling, and on-site surveys to estimate carbon storage and sequestration in urban parks. The results show that the average carbon storage per unit area in the entire park was 15.3 tons of carbon per hectare, ranging from a minimum of 5.0 to a maximum of 21.4 tons per hectare. For the planted area, the average carbon storage was 8.6 tons per hectare. Grids with green areas dominated by broad-leaved trees and closed canopy cover had the highest carbon sequestration and storage values. The planting area ratio and the type of trees planted were found to directly influence the carbon sequestration capacity per unit area of urban parks. This study stands out from previous research by conducting a detailed area-based comparison and analysis of carbon sequestration capacity in urban parks using sophisticated measurement techniques. The findings offer direct insights into strategies and policies for securing future urban carbon sinks and can be of practical use in this regard.

Research Progress in Methane Storage Using the Metal-Organic Frameworks

The improvement of social production and social technology in recent times has led to a gradual increase in the worldwide energy demand. In addition to the issue of shortage, burning fossil fuels like traditional oil produces a significant quantity of greenhouse emissions, which contribute to global climate change. In today's world, natural gas is an essential clean energy source. These days, liquefied natural gas (LNG), adsorbed natural gas (ANG), and compressed natural gas (CNG) are the most widely utilized storage techniques. ANG is the most promising method since it offers advantages over other approaches, including room temperature storage, safety, dependability, efficiency, and environmental protection. The most popular adsorbent materials at the moment are metal-organic frameworks (MOFs), which are characterized by a high specific surface area and porosity, a topological structure and pore size that can be adjusted, strong solvent stability, and heat stability. For this reason, the structural traits and techniques of the production of MOFs are first summarized in this article. The methane adsorption process was then clarified. Ultimately, the use of MOFs in methane storage was highlighted to offer some guidance for the advancement of MOFs as adsorbent materials in the future.

Selenium-modified activated coke: a high-capacity and facile designed Hg0 adsorbent for coal-fired flue gas.

The substantial amount of mercury emissions from coal-fired flue gas causes severe environmental contamination. With the Minamata Convention now officially in force, it is critical to strengthen mercury pollution control. Existing activated carbon injection technologies suffer from poor desulfurization performance and risk secondary-release risks. Therefore, considering the potential industrial application of adsorbents, we selected cost-effective and readily available activated coke (AC) as the carrier in this study. Four metal selenides-copper, iron, manganese, and tin-were loaded onto the AC to overcome the application problems of existing technologies. After 120min of adsorption, the CuSe/AC exhibited the highest efficiency in removing Hg0, surpassing 80% according to the experimental findings. In addition, the optimal adsorption temperature window was 30-120°C, the maximum adsorption rate was 2.9 × 10-2mg·g-1·h-1, and the effectiveness of CuSe/AC in capturing Hg0 only dropped by 5.2% in the sulfur-containing atmosphere. The physicochemical characterization results indicated that the AC surface had a uniform loading of CuSe with a nanosheet structure resembling polygon and that the Cu-to-Se atomic ratio was close to 1:1. Finally, two possible Hg0 reaction pathways on CuSe/AC were proposed. Moreover, it was elucidated that the highly selective binding of Hg0 with ligand-unsaturated Se- was the key factor in achieving high adsorption efficiency and sulfur resistance in the selenium-functionalized AC adsorbent. This finding offers substantial theoretical support for the industrial application of this adsorbent.

Use of the Gas Emission Site Type Method in the Evaluation of the CO2 Emissions in Raised Bogs

Peatbogs are important in global greenhouse gas budget estimates. The main aim of the work was to assess the amount of greenhouse gas emissions based on the Greenhouse Gas Emission Site Type (GEST) method and compare them with actual field measurements. The research was carried out in selected peatbog areas in the coastal zone of northern Poland. The proposed method allowed us to assess whether the restoration treatments carried out in peatbogs contributed to the emission of these gases. The results of this research using the GEST method indicate that, in the studied peatbog areas, the reduction in CO2 emissions was approximately 12%. These results were compared with actual measurements of greenhouse gas emissions made using the chamber method in 2018. The average CO2 emission for the entire peatbog was 16,338.7 t CO2-eq./yr. Comparing this result with the result obtained using the GEST method, it should be stated that it is lower by 2.464.1 t CO2-eq./year, which gives an approximately 13% overestimation of the result by the Gas Emission Site Type method.

The Spatial–Temporal Emission of Air Pollutants from Biomass Burning during Haze Episodes in Northern Thailand

Air pollutants from biomass burning, including forest fires and agricultural trash burning, have contributed significantly to the pollution of the Asian atmosphere. Burned area estimates are variable, making it difficult to measure these emissions. Improving emission quantification of these critical air pollution sources requires refining methods and collecting thorough data. This study estimates air pollutants from biomass burning, including PMs, NOX, SO2, BC, and OC. Machine learning (ML) with the Random Forest (RF) method was used to assess burned areas in Google Earth Engine. Forest emissions were highest in the upper north and peaked in March and April 2019. Air pollutants from agricultural waste residue were found in the lower north, but harvesting seasons made timing less reliable. Biomass burning was compared to the MODIS aerosol optical depth (AOD) and Sentinel-5P air pollutants, with all comparisons made by the Pollution Control Department (PCD) Thailand air monitoring stations. Agro-industries, mainly sugar factories, produce air pollutants by burning bagasse as biomass fuel. Meanwhile, the emission inventory of agricultural operations in northern Thailand, including that of agro-industry and forest fires, was found to have a good relationship with the monthly average levels of ambient air pollutants. Overall, the information uncovered in this study is vital for air quality control and mitigation in northern Thailand and elsewhere.

Anthropogenic dissolved organic matter accumulation fuels greenhouse gas diffusive emissions in urban lakes along trophic state levels

Lakes are an important reservoir of dissolved organic matter (DOM) and also emit large amounts of greenhouse gases (GHGs), such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Yet, the linkages between quantity and quality of DOM and GHGs emissions are still not well understood. Hence, we investigated how DOM optical properties mediated dissolved GHGs levels and diffusive fluxes along a trophic state level in subtropical urban lakes, China. Our study showed that anthropogenic protein-like DOM (tryptophan- and tyrosine-like components) dominated the DOM pools, comprising 62% of total fluorescence DOM (FDOM). Partial least squares path modeling (PLS-PM) showed that the direct contribution of relative abundance of DOM to CO2 and CH4 diffusion in the highly-eutrophic state was higher than in the mesotrophic and lightly/moderately-eutrophic states. In particular, protein-like DOM primarily accounted for CO2 variations (44%-49%), while humic- and protein-like DOM contributed comparably to CH4 variations (38%-40% vs. 36%-40%). Furthermore, in the lightly/moderately- and highly-eutrophic states, nutrients contributed to DOM, CO2 and CH4 in both direct and indirect ways, while phytoplankton (chlorophyll a, Chl-a) negatively and directly affected carbon emissions. However, only nitrogen content directly contributed to N2O production and diffusion in the mesotrophic and lightly/moderately-eutrophic states. We concluded that protein-like DOM enrichment had the potential to trigger CO2 and CH4 diffusion and this process was largely mediated by nutrient levels. N2O diffusion was found to be mainly influenced by nitrogen loadings and forms. Our findings highlight the urgency for effective strategies to reduce anthropogenic DOM inputs and control nutrient levels to reduce GHGs diffusion in highly human-impacted aquatic systems.