Daily Emissions Research Articles

Ammonia emission estimates using CrIS satellite observations over Europe

Abstract. Over the past century, ammonia (NH3) emissions have increased with the growth of livestock and fertilizer usage. The abundant NH3 emissions lead to secondary fine particulate matter (PM2.5) pollution, climate change, and a reduction in biodiversity, and they affect human health. Up-to-date and spatially and temporally resolved information on NH3 emissions is essential to better quantify their impact. In this study we applied the existing Daily Emissions Constrained by Satellite Observations (DECSO) algorithm to NH3 observations from the Cross-track Infrared Sounder (CrIS) to estimate NH3 emissions. Because NH3 in the atmosphere is influenced by nitrogen oxides (NOx), we implemented DECSO to estimate NOx and NH3 emissions simultaneously. The emissions are derived over Europe for 2020 on a spatial resolution of 0.2°×0.2° using daily observations from both CrIS and the TROPOspheric Monitoring Instrument (TROPOMI; on the Sentinel-5 Precursor (S5P) satellite). Due to the limited number of daily satellite observations of NH3, monthly emissions of NH3 are reported. The total NH3 emissions derived from observations are about 8 Tg yr−1, with a precision of about 5 %–17 % per grid cell per year over the European domain (35–55° N, 10° W–30° E). The comparison of the satellite-derived NH3 emissions from DECSO with independent bottom-up inventories and in situ observations indicates a consistency in terms of magnitude on the country totals, with the results also being comparable regarding the temporal and spatial distributions. The validation of DECSO over Europe implies that we can use DECSO to quickly derive fairly accurate monthly emissions of NH3 over regions with limited local information on NH3 emissions.

Monitoring European anthropogenic NOx emissions from space

Abstract. Since the launch of TROPOMI on the Sentinel-5 Precursor (S5P) satellite, NO2 observations have become available with a resolution of 3.5× 5 km, which makes monitoring NOx emissions possible at the scale of city districts and industrial facilities. For Europe, emissions are reported on an annual basis for country totals and large industrial facilities and made publicly available via the European Environment Agency (EEA). Satellite observations can provide independent and more timely information on NOx emissions. A new version of the inversion algorithm DECSO (Daily Emissions Constrained by Satellite Observations) has been developed for deriving emissions for Europe on a daily basis, averaged to monthly mean maps. The estimated precision of these monthly emissions is about 25 % for individual grid cells. These satellite-derived emissions from DECSO have been compared to the officially reported European emissions and spatial–temporal disaggregated emission inventories. The country total DECSO NOx emissions are close to the reported emissions and the emissions compiled by the Copernicus Atmosphere Monitoring Service (CAMS). Comparison of the spatially distributed NOx emissions of DECSO and CAMS showed that the satellite-derived emissions are often higher in cities, while they are similar for large power plants and slightly lower in rural areas.

Multi-objective optimization of a hydrogen supply chain network: Wind and solid biomass as primary energy sources for the public transport in Sicily

Europe’s heavy reliance on diesel power for nearly half of its railway lines, for both goods and passengers, has significant implications for carbon emissions. To address this challenge, the European Union advocates for a shift towards hydrogen-based mobility, necessitating the development of robust and cost-effective hydrogen supply chains at regional and national levels. Leveraging renewable energy sources such as wind farms and solid biomass could foster the transition to sustainable hydrogen-based transportation. In this study, a mixed-integer linear programming approach integrated with an external heavy-duty refueling station analysis model is employed to address the optimal design of a new hydrogen supply chain. Through multi-objective optimization, this study aimed to minimize the overall daily costs and emissions of the supply chain. By applying the model to a case study in Sicily, different scenarios with varying supply chain configurations and wind curtailment factors were explored. The findings revealed that increasing the wind curtailment factor from 1% to 2% led to reductions of 12% and 15% in the total daily emission costs and network costs, respectively. Additionally, centralized biomass-based plants dominated hydrogen production, accounting for 96% and 94% of the total production under 1% and 2% wind curtailment factors, respectively. Furthermore, transporting gaseous hydrogen via tube trailers proved more cost effective than using tanker trucks for liquid hydrogen when compressed gaseous hydrogen is required at the dispenser of forecourt refueling stations. Finally, the breakdown of the levelized cost for the hydrogen refuelling station strongly depends on the form of hydrogen received at the gate, namely, liquid or gaseous. Specifically, for the former, the dispenser accounts for 60% of the total cost, whereas for the latter, the compressor is responsible for 58% of the total cost. This study highlights the importance of preliminary and quantitative analyses of new hydrogen supply chains through model-based optimization.

Bridging chance-constrained and stochastic optimization for risk estimation of virtual energy hubs dominated by hybrid vehicles under diverse uncertainties: To improve economic sustainability

Virtual energy hubs (VRHBs) combine dispersed energy resources to enhance the efficient use of various energy sources, grid flexibility, and renewable energy adoption. This paper investigates the adoption of VRHBs with power-to-gas (P2G) facilities and hybrid electric-compressed natural gas vehicles (HNGEVs) in the presence of renewable resources to effectively tackle carbon emissions and increase the flexibility of VRHBs. The proposed P2G facility is constructed from carbon capture to collect carbon pollution from fossil fuel resources and HNGEVs to recycle into synthesis gas in a methanation plant. Furthermore, due to severe uncertainties due to renewable resources and energy consumption intensity, the proposed framework utilizes stochastic-driven chance-constrained optimization, which enables decision-makers in VRHBs to account for the probabilistic nature of uncertainties. Chance-constrained optimization explicitly considers uncertainty through probabilistic constraints, providing a more robust and reliable solution compared to deterministic methods. By modeling uncertainties using chance-constrained programming, VRHBs can manage the economic risks under unexpected worst realizations in uncertainties. Based on the results, the P2G facility leads to a significant reduction in the daily emissions by 63.06 % and 42.77 % observed under no-risk and full-risk conditions, respectively. In addition, the suggested chance-constrained method resulted in a major decrease of 69 % in overall operational costs.

Evaluation of Increasing Dietary Concentrations of a Multi-Enzyme Complex in Feedlot Lambs' Rations.

The objective of this study was to evaluate the effects of increasing levels of the M-E complex (xylanase, glucanase, cellulase, and invertase) Optimax E® on the performance of growing lambs, their digestibility, and their rumen microbiota, and to estimate NEm, NEg, and ruminal methane levels. Forty lambs (Katahdin x Dorset; 22.91 ± 4.16 kg) were randomly assigned to dietary concentrations of ME (0, 0.2, 0.4, and 0.8% DM) and fed individually for 77 days. Increasing M-E improved feed conversion (p < 0.05) as well as NEm and NEg (p < 0.05), which were associated with increased in vivo DM and NDF digestion (linear and quadratic p < 0.01). Few microbial families showed abundancy changes (Erysipelotrichaceae, Christensenellaceae, Lentisphaerae, and Clostridial Family XIII); however, the dominant phylum Bacteroidetes was linearly reduced, while Firmicutes increased (p < 0.01), resulting in a greater Firmicutes-to-Bacteroidetes ratio. Total Entodinium showed a quadratic response (p < 0.10), increasing its abundancy as the enzyme dose was augmented. The daily emission intensity of methane (per kg of DMI or AGD) was reduced linearly (p < 0.01). In conclusion, adding the M-E complex Optimax E® to growing lambs' diets improves their productive performance by acting synergistically with the rumen microbiota, modifying the Firmicutes-to-Bacteroidetes ratio toward more efficient fermentation, and shows the potential to reduce the intensity of greenhouse gas emissions from lambs.

ПЛАНИРОВАНИЕ И РЕАЛИЗАЦИЯ СТРОИТЕЛЬНЫХ РАБОТ ПРИ ТОЧЕЧНОЙ ЗАСТРОЙКЕ ДЛЯ ОПРЕДЕЛЕНИЯ УДЕЛЬНЫХ ПЫЛЕВЫХ ВЫБРОСОВ

Abstract. The construction industry is one of the significant sources of environmental damage. The effects of dust pollution have an impact on all stages of the building's life cycle, from the start of work on the construction site to completion, operation and demolition. Despite the fact that the stage of work implementation does not last long in comparison with other stages of the life cycle of an investment and construction project, the construction stage has a number of significant impacts on the environment. The development of the construction industry requires the comprehensive assessment and investigation of the construction site as a source of pollu-tion, identification and evaluation of all sources of dust pollution on the construction site, the processes of dust pollution propagation in an urban environment. &#x0D; The article offers an analysis of data on field studies of the degree of atmospheric air pollution in the territory of Rostov-on-Don, an analysis of the volume of construction dust emissions from construction works, a method for calculating control over the implementation of construction processes and the spread of dust pollution generated in the atmospheric air, indi-cators of the maximum, average daily emission concentration of fine dust PM2.5 and PM10 formed from construction production and the ability to control the level of pollution on the construction site. There are no fundamental studies on the calculation of dust emission from construction works during spot construction. Based on the field studies conducted earlier using the Handheld 3016 particle counter, data on dust emissions from construction processes under various climatic influences were obtained.&#x0D; Subject: analysis of data on field studies of the degree of atmospheric air dustiness in the territory of Rostov-on-Don, the volume of emissions of construction dust from construction work on construction sites in urban conditions.&#x0D; Materials and methods: conducting systematic measurements of the degree of dust in Rostov-on-Don using the Lighthouse Handheld 3016 IAQ manual particle counter, taking into account typical climatic, heterogeneous factors of the territory on which the city is located. Systematic monitoring of several construction sites where residential complexes similar in technology and conditions were built in the most dusty area of Rostov-on-Don and the selection of five construction sites for the development of a calendar schedule for construction and installation work, taking into account specific emissions of dust particles and climatic conditions for calculating the dust pollution factor.&#x0D; Results: an effective approach that can be usefully applied to the determination of dust emissions on the construction site, to calculate the gross emission of dust released on the site from construction work, daily indicators of the maximum single and average daily concentration, as well as to dust dispersion with the determination of zones exceeding the MPC indicators is the use of network modeling with further calendarization to predict these types of indicators during the design and implementation of construction production.&#x0D; Conclusions: based on the data obtained, it can be concluded that with the existing implemented organization and technology of work, the concentration of fine dust particles are within the MPC, but given the background concentrations of atmospheric air in Rostov-on-Don, these indicators in a particular area will exceed the permissible MPC. The process of using existing SMR models taking into account specific dust emissions, the development of a schedule of dust pollution of a specific territory taking into account natural and climatic factors and the determination of zones of excess of MPC indicators in the process of modeling the dispersion of fine dust particles beyond the fence boundaries of the construction site will make it possible to draw up a passport of environmental safety of the nearby urbanized territory during spot development. Daily determination of dust pollution indicators will allow you to adjust the schedule of construction work. Modeling the dispersion of construction dust, taking into account the direction and speed of the wind, will make it possible to monitor the zones of exceeding the MPC of the neighboring territory on a daily basis and adjust measures to extinguish dust emissions at the construction site.

Modelling of Aircraft Non-CO2 Emissions Using Freely Available Activity Data from Flight Tracking

The objective of this work is to develop a new modelling tool to quantify non-CO2 emissions from aircraft during the landing and take-off (LTO cycle) considering the following criteria: (i) utilising freely available activity data; (ii) using widely recommended emission factors; (iii) providing emissions with the spatial and temporal resolution required for local air quality assessment. Freely available flight tracking data obtained from Flightradar24 and emission factors from the European Environment Agency (EEA/EMEP) are considered for the algorithm implementation and tested for Lisbon airport. The analyses of aircraft emissions during various flight stages reveal that HC and CO are mostly emitted during taxiing (93%), while NOX and PM are mostly produced during climb-out (48% and 35%, respectively). Sensitivity analysis, by using simplified activity data from publicly available databases against detailed engine models and emission factors, showed variations in the daily emissions of less than 13% in the case of CO and NOX, but up to 34% for HC and PM. The modelling approach based on freely available activity data developed in this work may offer valuable insights for quantifying airport emissions, providing crucial inputs for air quality assessment, and supporting the definition of mitigation strategies aimed at enhancing sustainability in aviation practices.

Sustainable urban logistics: A case study of waterway integration in Amsterdam

This paper tackles the growing challenges in urban logistics by presenting an optimal distribution network that integrates urban waterways and last-mile delivery, tailored for cities boasting extensive waterway networks. We examine Amsterdam's city center as a case study, prompted by the strain on quay walls, congestion, and emissions, urging a reevaluation of its urban logistics design. We formulate the problem as a two-echelon location routing problem with time windows and introduce a hybrid solution approach for effective resolution. Our algorithm consistently outperforms existing methods, with a superior solution quality, demonstrating its effectiveness across established and newly developed benchmark instances. In our case study, we evaluate the benefits of transitioning from a roadway-centric to a waterway-based system, showcasing significant cost savings (approximately 28 %), reductions in vehicle weight (approximately 43 %), and minimized travel distances (approximately 80 %) within the city center. The integration of electric vehicles enhances environmental sustainability, resulting in a total daily emission reduction of 43.46 kg. Our study underscores the untapped potential of inland waterways in easing urban logistics challenges. Inspired by Amsterdam's experience, global cities can adopt innovative approaches for sustainable logistics, providing valuable insights for managers striving to enhance efficiency, cut costs, and promote sustainable transportation practices.

Unveiling causal dynamics and forecasting of urban carbon emissions in major emitting economies through multisource interaction

Mitigating city carbon emissions in major emitting economies is vital for sustainable development. This study exploits the complex interactions among major sources of emissions to address two research gaps in city carbon neutrality: causal discovery and forecasting in high-emission economies through developing a novel two-stage algorithm. In the initial phase, the algorithm intricately unravels the causal dynamics of emissions from thousands of urban sources. Next, it employs a deciphered causal matrix to forecast city emission trends, leveraging insights from the prior analysis. It outperformed other advanced algorithms in tests using daily emission data from China, the European Union, and the United States. Evaluations, including prediction errors, Taylor diagrams, Diebold-Mariano tests, and ablation experiments, validate its superior ability in identifying inter-city carbon emission interactions and more accurately making 1-step and 5-step advanced predictions. This research confirms that analyzing interactions among major urban emission sources can effectively reveal the causal patterns of carbon emissions among cities and predict future trends. It also finds that while the causal network traits of urban emissions in leading carbon-emitting economies show consistency, distinct differences exist. Leveraging these insights, this study offers tailored policy implications for collaborative carbon emission reduction in cities within these economies.

Predicting air pollutant emissions of the foundry industry: Based on the electricity big data

Industrial enterprises are one of the largest sources of air pollution. However, the existing means of monitoring air pollutant emissions are narrow in coverage, high in cost, and low in accuracy. To bridge these gaps, this study explored a predicting model for air pollutant emissions from foundry industries based on high-accuracy electricity consumption data and continuous emission monitoring system (CEMS). The model has then been applied to the calculation of air pollutant emissions from foundries without CEMS and the optimization of air pollutant emission temporal allocation factors. The results reveal that electricity consumption and PM emissions during the 2022 Beijing Winter Olympics have the same ascending and descending relationship. Furthermore, a cubic polynomial model between electricity consumption and flue gas flow is established based on the whole year data of 2021 (R2 = 0.85). The relative errors between the PM emissions calculated by the model and the emission factor method are small (−17.09–24.12 %), and the results from the two methods revealed a strong correlation (r = 0.93, p < 0.01). In addition, the monthly PM emissions from foundries are mainly concentrated in spring and winter, and the daily emissions on weekends are significantly lower than those on workdays. These results can be useful for environmental regulation and optimization of air pollutant emission inventories of foundry industry.

A portable low-cost device to quantify advective gas fluxes from mofettes into the lower atmosphere: First application to Starzach mofettes (Germany)

In this study, we introduce a portable low-cost device for in situ gas emission measurement from focused point sources of CO2, such as mofettes. We assess the individual sensors’ precision with calibration experiments and perform an independent verification of the system’s ability to measure gas flow rates in the range of liters per second. The results from one week of continuous CO2 flow observation from a wet mofette at the Starzach site is presented and correlated with the ambient meteorological dynamics. In the observed period, the gas flow rate of the examined mofette exhibits a dominant cycle of around four seconds that is linked to the gas rising upwards through a water column. We find the examined mofette to have a daily emission of 465 kg ±16 %. Furthermore, two events were observed that increased the flow rate abruptly by around 25 % within only a few minutes and a decaying period of 24 hours. These types of events were previously observed by others at the same site but dismissed as measurement errors. We discuss these events as a hydrogeological phenomenon similar to cold-water geyser eruptions. For meteorological events like the passages of high pressure fronts with steep changes in atmospheric pressure, we do not see a significant correlation between atmospheric parameters and the rate of gas exhalation in our one-week time frame, suggesting that on short timescales the atmospheric pumping effect plays a minor role for wet mofettes at the Starzach site.

Influence of tillage methods on carbon dioxide emissions in spring wheat crops

The agriculture intensification is associated with an increase in anthropogenic load on arable soils. Tillage leads to disruption of the natural process of soil formation in connection with which the soil biota activity changes and the carbon cycle is disrupted. The purpose of the study is to establish the effects of the tillage method on the carbon dioxide emission in spring wheat crops. The emission of carbon dioxide during the growing season of spring wheat varies significantly depending on the soil processing method and temperature. At the beginning of spring wheat development, the daily emission of carbon dioxide during the dump and subsurface tillage method does not exceed 36.0 and 36.2 CO2 kg/ha using zero technology provides a reduction in production CO2 production to 27.8 kg/ha*day. With an increase in soil temperature by July 24, the daily emission increases on a dump and subsurface background to 105.5 and 106.0 CO2 kg/ha*day, on a zero background to 95.4 CO2 kg/ha*day. In the future, it decreases. The total carbon losses during the dump and subsurface tillage methods are 2829 and 2793 kg/ha, the use of zero tillage technology reduces carbon losses in grain agrocenosis by 18%.

A model predictive control application for air quality management

This study introduces a model predictive control methodology to determine optimal measures for mitigating air pollution, assisting Local Authorities in policy development. Anchored in an auto-regressive model, it analyzes dynamic air quality patterns over a defined timeframe using daily observed pollutant concentration, meteorological variables, and estimated emission data. Employing model predictive control methodology, the approach aims to optimize daily emission reductions. Evaluated in Milan, a heavily polluted European city, the findings highlight the methodology's potential as a robust tool for Local Authorities, enabling informed decisions in crafting efficient air quality management strategies, in the specific context ofNO2.

Assessment of tobacco (Nicotiana tabacum) and N. benthamiana as biofactories of irregular monoterpenes for sustainable crop protection

Irregular monoterpenes are important precursors of different compounds employed in pest control such as insecticides and insect sex pheromones. Metabolically engineered plants are appealing as biofactories of such compounds, but specially as potential live biodispensers of related bioactive volatiles, which could be continuously emitted to the environment from different plant tissues. Here we assess the use of cultivated tobacco and Nicotiana benthamiana as biofactories for the irregular monoterpenes chrysanthemol and lavandulol. We evaluate the impact of high levels of constitutive metabolite production on the plant physiology and biomass, and their biosynthetic dynamics for different plant tissues and developmental stages. As an example of an active pheromone compound, we super-transformed the best lavandulol-producing tobacco line with an acetyl transferase gene to obtain a tobacco lavandulyl acetate biodispenser emitting up to 0.63 mg of lavandulyl acetate per plant every day. We estimate that with these volatile emission levels, between 200 and 500 plants per hectare would be sufficient to ensure a daily emission of pheromones comparable to commercial lures. This is an important step towards plant-based sustainable solutions for pest control, and it lays the ground for further developing biofactories for other irregular monoterpenoid pheromones, whose biosynthetic genes are yet unknown.

Release of Microplastics from Urban Wastewater Treatment Plants to Aquatic Ecosystems in Acapulco, Mexico

Contamination of aquatic ecosystems by microplastics (MPs) is mainly due to the release of high levels of MP particles from treated effluents by wastewater treatment plants (WWTPs). Due to the lack of policies and regulations establishing criteria for the control and elimination of MPs from WWTP effluents, this research evaluates the presence of 38 and 150 µm MPs in influents and effluents from three WWTPs in the port of Acapulco, Mexico. Optical microscopy and Fourier transform infrared spectroscopy revealed that the MPs were polyethylene, polypropylene, polyethylene terephthalate, and polyvinyl chloride. MP removal efficiencies of 82.5–98.7% (38 µm) and 86.8–97.5% (150 µm) were achieved. Moreover, the MP average daily emissions to the receiving bodies of the three WWTPs ranged from 9.5 × 106 to 4.70 × 108 particles, while the annual emissions ranged from 3.05 × 109 to 1.72 × 1011 particles. This work reveals the urgency of implementing regulatory policies to avoid the continuous emission of MPs into aquatic ecosystems from WWTPs in Acapulco, Mexico.

Effects of environmental and housing system factors on ammonia and greenhouse gas emissions from cattle barns: A meta-analysis of a global data collation

This study provides a meta-analysis on the relationships between cattle barn CH4, NH3 and N2O emission rates and their key drivers (i.e., housing type, floor type, environmental conditions). Understanding these relationships is essential to reduce uncertainties in emission inventories and suggest targeted mitigation measures. The total number of daily emission rates included in the analysis was 139 for CH4, 293 for NH3 and 100 for N2O emissions. Emission rates in the database showed a large variation with 45–803.5 g/LU d−1 for CH4, 0.036–146.7 gN LU−1 d−1 for NH3, and 0.002–18 gN LU−1 d−1 for N2O emissions. Despite the high emission variability, significant effects were identified·NH3 showed positive correlation with air temperature; NH3 emissions differed between housing types but not between floor types·NH3 emissions from tied stalls were lower than the ones from cubicle housing regardless of the floor type. Additionally, NH3 emissions from loose housings were lower than the ones from cubicle housing·NH3 and N2O emission rates from temperate wet zones were lower than the ones from temperate dry zones. CH4 emission rates were affected by environmental factors only and not by housing and floor type, showing negative correlation with air temperature and humidity. The factors investigated can be suggested as ancillary variables and descriptors when cattle barn emissions are measured, in order to make best use of emission data. Country-specific data of these key drivers can be included into national inventories to adapt them to different agroecosystems and support targeted policies.

Design of energy big data and carbon emission monitoring system based on perceptron model in the context of carbon neutral and carbon peaking

Abstract The big energy data and carbon emission monitoring system is designed to collect carbon emission-related data for pollution gas management. This paper constructs a carbon emission monitoring system in the context of carbon neutrality and peaking. A multi-layer perceptron algorithm is introduced based on the principle of perception, and a BP-MLP neural network model is proposed by optimizing the perceptron weights using BP neural network. For the sensors in the carbon emission monitoring system, the node redundancy is processed, and the optimal sensor distribution is proved by using the correlation coefficient. Finally, the evaluation analysis of the carbon monitoring system was carried out in three aspects: relevance coefficient de-redundancy, number of iterations and daily emissions. The results show that when the correlation threshold is 0.8, the sensor distribution of the monitoring system can satisfy the monitoring under various wind conditions, and when the number of iterations is 600, the difference between the real value and the monitored value is only 3.63% and the daily emission peaks at 5.243 mg/m3 at 14:00 a.m. This shows that the carbon emission monitoring system constructed based on the BP-MLP model can effectively collect and analyze carbon emission data. Data collection and analysis, and provide corresponding data support for the management of gas pollution.

Emission leakage and the effectiveness of regional environmental regulation in China

This paper examines the emission leakage by the regional environmental regulation applied in Beijing and its surrounding area. Using the unique firm-level daily emission data, we develop a novel model to estimate the size of immediate leakage through the price mechanism. Our empirics find that leakage offsets about 60% of the total amount of emission reduction in the regulated region and the aggregate emission reduction is less than two percent. We also use the iron and steel industry as an example to show how the prices of input and output factors respond to the regulation.

Projected smoke impacts from increased prescribed fire activity in California's high wildfire risk landscape

California has experienced multiple catastrophic wildfires over the last decade, fostering the need for increased forest management and understanding of the health impacts of wildfire and prescribed fire smoke. We calculated air quality impacts from past wildfires and prescribed fires and estimated how air quality may be affected under a future scenario in which prescribed fire is proposed to more than double. We merged satellite and agency records to establish a 10-year (2008–2017) baseline wildfire and prescribed fire inventory for California. Daily emissions and dispersion modeling were used to evaluate relative contributions from wildfire and prescribed fire. We scaled up prescribed burns to reach the >200,000 ha per year target, modeled over eight cycles (years) to encompass 1.6 million hectare of California Department of Forestry and Fire Prevention Priority Landscapes 4&5. We found that baseline wildfire contributes up to 20x more smoke PM2.5 concentrations than baseline prescribed fire. Target prescribed fire scenario smoke concentrations are anticipated to be ∼3x that of baseline prescribed fire. Baseline and target prescribed fire scenarios do not contribute sufficient PM2.5 to exceed air quality standards from smoke alone at the community (ZIP code) level. Baseline wildfires contribute enough PM2.5 to cause unhealthy air quality, even without contributions from ambient background PM2.5. The target prescribed fire scenario only exceeds the baseline smoke from wildfire and prescribed fire in 4% of California ZIP codes. Overall, we found that air quality is impacted significantly more from wildfires than prescribed fires, past and proposed, for the majority of California.

Measurement of Ammonia and Hydrogen Sulfide Emission from Three Typical Dairy Barns and Estimation of Total Ammonia Emission for the Chinese Dairy Industry.

There is an urgent need for accurate measurement for emissions of ammonia (NH3) and hydrogen sulfide (H2S) in dairy barns in order to obtain reliable emission inventories and to develop and evaluate abatement strategies. This experiment was performed on three dairy farms in central China during 14 consecutive days in the winter 2020. Concentrations of NH3 and H2S were measured every two hours. The samples were taken inside and outside of barns from 7 sites at two heights (at floor and 1.5 over the floor). The results show that the average NH3 concentration was 2.47 mg/m3 with a maximum of 4.62 mg/m3, while the average H2S concentration was 0.179 mg/m3 with a maximum of 0.246 mg/m3. Lactating cows produced significantly more NH3 (3.73 mg/m3 versus 2.34 mg/m3) and H2S (0.24 mg/m3 versus 0.14 mg/m3) than non-lactating cows. NH3 and H2S concentrations were higher at 0 m than at 1.5 m, especially during the day. In addition, the average daily emission rates per animal unit (AU = 500 kg weight) were 23.5 g and 0.21 g for NH3 and H2S, respectively. The emission rate for NH3 was then used to extrapolate the NH3 emission from the Chinese dairy production. Our estimation for 2016 was 0.45 Tg, and it could reach 1.35 Tg by 2050. These numbers reflected our first attempt to calculate emission inventories for the Chinese dairy industry. Our results also suggest that more concrete measures must be taken to reduce the uncertainties of NH3 emissions from dairy cow production in China.