Annual Inventory Research Articles

Biomass burning CO emissions: exploring insights through TROPOMI-derived emissions and emission coefficients

Abstract. Emissions from biomass burning are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometres downwind. These emissions can be estimated by a bottom-up approach that relies on fuel consumed and standardized emission factors. Emissions are also commonly derived with a top-down approach, using satellite-observed fire radiative power (FRP) as a proxy for fuel consumption. Biomass burning emissions can also be estimated directly from satellite trace gas observations, including carbon monoxide (CO). Here, we explore the potential of satellite-derived CO emission rates from biomass burning and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tropospheric Monitoring Instrument) high-spatial-resolution satellite datasets to derive burning CO emissions directly for individual fires between 2019 and 2021 globally. Using synthetic data (with known emissions), we show that the direct emission estimate methodology has a 34 % uncertainty for deriving CO emissions (and a total uncertainty of 44 % including wind and CO column uncertainty). From the TROPOMI-derived CO emissions, we derive biome-specific emission coefficients (emissions relative to FRP) by combining the direct emission estimates and the satellite-observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS). These emission coefficients are used to establish annual top-down CO emission inventories from biomass burning, showing that Southern Hemisphere Africa has the highest CO biomass burning emissions (over 25 % of global total of 300–390 Mt(CO) yr−1 between 2003–2021), and almost 25 % of global CO biomass burning emissions are from broadleaved evergreen tree fires. A comprehensive comparison between direct estimates, top-down and bottom-up approaches, provides insight into the strengths and weaknesses of each method: FINN2.5 has higher CO emissions, by a factor between 2 and 5, than all other inventories assessed in this study. Trends over the past 2 decades are examined for different regions around the globe, showing that global CO biomass burning emissions have, on the whole, decreased (by 5.1 to 8.7 Mt(CO) yr−1), where some regions experience increased and others decreased emissions.

Widespread and Rapid Activities of Retrogressive Thaw Slumps on the Qinghai‐Tibet Plateau From 2016 to 2022

AbstractRetrogressive thaw slumps (RTSs), formed by abrupt degradation of ice‐rich permafrost, are widely distributed on the Qinghai‐Tibet Plateau, causing infrastructure damage and enhancing soil carbon emissions. We compiled annual RTS inventories across the plateau from 2016 to 2022 using a deep‐learning‐aided method to quantify the spatial‐temporal variations. We found that RTS‐affected locations increased from 1,592 to 3,805 in 2016–2022, which increased affected areas by 2.8 times from 1,714 to 6,507 ha. The most active initiation and expansion periods were in 2016–2017 and 2018–2019. RTSs tend to be clustered, showing local heterogeneity among clusters characterized by various responses toward high temperatures and precipitation and tendencies to be on different topography and vegetation types. This research reveals the rapid development, wide distribution and regional heterogeneity of RTS activities, serving as a crucial step toward understanding how RTSs respond to climate change and regional environmental varieties.

Annual improved maps to understand the complete evolution of 9 thousand lakes on the Tibetan plateau in 1991–2023

Rapid changes in the densely distributed lakes on the Tibetan Plateau (TP) reflect the responses of terrestrial water resources to climate change. Timely and accurate monitoring of lake dynamics is essential for formulating adaptation strategies to manage water and protect public facility safety sustainably. Interfered by the numerous glaciers and snow mountains and limited by the acquisition and computing capacities of massive satellite data, annual inventories of all the lakes ranging from mini to large on the TP are still lacking. Here, we annually mapped these lake areas using all the Landsat imagery, a robust algorithm for detecting surface water according to multiple spectral indices, and Google Earth Engine. We further proposed an effective approach for accurately identifying the glaciers, snow, and mountain shadows in satellite imagery by introducing the characteristics of image luminosity and terrain slope, and removing their data noise remained in the lake maps to generate an annual precise dataset (Lake_TP) of the approximately 9,000 lakes over 0.1 km2 on the TP during 1991–2023. We revealed a rapid expansion of lakes with significant spatial heterogeneity, with 6,590 newly increased and 2,851 disappeared lakes found. The total lake areas (554.1 km2/yr) and numbers (77.9/yr) continuously and significantly increased in the period. The growth in lake numbers dominated by small lakes mainly happened before 2005, while the increases in lake areas dominated by large lakes lasted the whole period after 1995. The most significant increases in lake areas and numbers happened in the north of the Inner Basin and Yangtze, the hotspot of lake changes identified in the study. The dataset is expected to promote our understanding of the complete lake evolution process and the dynamic response of the cryosphere to the changing climate. The method proposed is also applicable to continuously monitoring the dynamics of lakes with higher accuracies in other alpine regions around the world. The Lake_TP dataset is publicly available at https://doi.org/10.5281/zenodo.10686952 (Zhou et al. 2024).

Application of the wildland fire emissions inventory system to estimate fire emissions on forest lands of the United States

BackgroundForests are significant terrestrial biomes for carbon storage, and annual carbon accumulation of forest biomass contributes offsets affecting net greenhouse gases in the atmosphere. The immediate loss of stored carbon through fire on forest lands reduces the annual offsets provided by forests. As such, the United States reporting includes annual estimates of direct fire emissions in conjunction with the overall forest stock and change estimates as a part of national greenhouse gas inventories within the United Nations Framework Convention on Climate Change. Forest fire emissions reported for the United States, such as the 129 Tg CO2 reported for 2022, are based on the Wildland Fire Emissions Inventory System (WFEIS). Current WFEIS estimates are included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2022 published in 2024 by the United States Environmental Protection Agency. Here, we describe WFEIS the fire emissions inventory system we used to address current information needs, and an analysis to confirm compatibility of carbon mass between estimated forest fire emissions and carbon in forest stocks.ResultsThe summaries of emissions from forests are consistent with previous reports that show rates and interannual variability in emissions and forest land area burned are generally greater in recent years relative to the 1990s. Both emissions and interannual variability are greater in the western United States. The years with the highest CO2 emissions from forest fires on the 48 conterminous states plus Alaska were 2004, 2005, and 2015. In some years, Alaska emissions exceed those of the 48 conterminous states, such as in 2022, for example. Comparison of forest fire emission to forest carbon stocks indicate there is unlikely any serious disconnect between inventory and fire emissions estimates.ConclusionsThe WFEIS system is a user-driven approach made available via a web browser. Model results are compatible with the scope and reporting needs of the annual national greenhouse gas inventories.

Implementation of the ABC Analysis to the Inventory Management

In an ever-changing market, inventory management is becoming a major problem that requires thorough planning, supervision, and control. If companies are well managed and have good planning and control, they can last a long time. Poor inventory management is very detrimental to the business as it can cause the company to fail to meet customer or consumer demands, which can lead customers to choose other companies that can meet their demands. Plan your stockpiles to maintain balance, avoid excess or deficiency, and control storage costs. ABC analysis, one of the most common inventory management techniques, divides inventory into three categories (A, B, and C) based on total annual product usage or total inventory cost based on the Pareto principle. The goal of this study is to use ABC analysis in the company's inventory management to identify products in categories A, B, and C based on profitability and sales factors. The research phases include data collection, data preprocessing, exploratory data analysis, ABC analysis, key product analysis, and explanatory analysis. According to the research findings, Segment A accounts for 18% of the total product and generates 78% of the overall profit. Segment B contributes 23% of the total number of items and 16% of all revenue.

Simulation studies of pollutant emissions from road vehicles using models for inventories of pollutant emissions

Countries are obliged by international regulations to conduct annual pollutant emissions inventories. Road transport is one of the sectors for which an inventory of pollutant emissions is carried out. Determining pollutant emissions from road transport is possible only by modeling these emissions – that is why unified emission models are used. In this work, the COPERT and HBEFA INFRAS software are used to determine pollutant emissions characteristics for various vehicle traffic models. The article presents the principles of modeling pollutant emissions from road vehicles. The rules for qualifying road vehicles into elementary and cumulative categories have been systematized. Models of road vehicle traffic and ways of taking them into account in modeling pollutant emissions are presented. The following emissions of pollutants harmful to the health and life of living organisms are considered: carbon monoxide, non-methane volatile organic compounds, nitrogen oxides and total suspended particles. The trends of the national annual emissions of the tested pollutants in the years 2000 – 2020 and the results of simulation tests of pollutant emissions models are presented.

Relationships between nutrients in the soil and nutritional status of Pinus taeda stands in southern Brazil

The characterization of nutrients availability in the soil and its relationship with nutritional status and productivity of Pinus taeda stands in southern Brazil is still of concern due the lack of research on this subject. We compared different methods to evaluate P, K, Ca, and Mg in the soil and in plant tissues. We performed the evaluations in six paired plots (control and fertilized) located on different soils conditions in southern Brazil. Availability of nutrients in the soil was obtained after extractions of soil solutions with the extractors Mehlich I (P, K), KCl 1 mol L−1 (Ca, Mg), and 1 % citric acid (P, K, Ca, Mg). Foliar and wood samples were collected to determine the P, K, Ca, and Mg contents in the trees’ tissues. The mean annual increment (MAI) of each plot was obtained from the annual inventories of the P. taeda stands. The contents of P, K, Ca, and Mg in the soil followed similar trends for the different extractors tested, with higher contents of Ca and Mg in the fertilized plots. No differences for P and K in the soil were observed between control and fertilized plots for both methods applied. The fertilized plots showed increase for Ca and Mg in the wood tissues but not in the needles. Positive correlation between wood and needle tissues was observed only for the P content. No correlation between the nutrient contents in the needles and in the soil was observed. But we found a positive correlation between the nutrient contents in the wood and in the soil, mainly for the 1% citric acid method. No correlation was found between the nutrients in the plant tissues and the productivity of Pinus taeda. Our results improved the knowledge concerning the nutrient dynamics in P. taeda stands in southern Brazil showing that citric acid is a good extractor for P, K, Ca, and Mg in the soil, and that determination of nutrients in the wood tissues is a potential method for nutritional diagnosis of P. taeda.

Investigating the impact of control strategies on the sulfur dioxide emissions of South Korean industrial facilities using an aircraft mass balance approach

We present top-down SO2 emission estimates from four large point sources, including two power plants, a steel mill, and a petrochemical industrial facility in Taean, South Korea. The airborne observations were conducted over two years, with three intensive observations in Fall 2019, Fall 2020, and Spring 2021. During this period, an active policy implementation to reduce air pollutant emissions from large industrial point sources in the region had been exercised, in addition to activity disruptions from the global COVID-19 pandemic. We quantify the observed SO2 emission reduction over the time period resulting from the policy implementation. The Continuous Emission Monitoring System (CEMS) datasets from the coal power plants were 1) validated by comparing them with emissions calculated using a top-down airborne mass balance method and 2) compared with the annual bottom-up national SO2 emission inventory. The comparisons illustrate that the policy implementation resulted in a 35% reduction in SO2 emissions from increased implementation of SO2 reduction technologies such as scrubbers. Finally, the COVID-19 pandemic did not cause a meaningful disruption in SO2 emissions from industrial sectors, such as steel mills and petrochemical manufacturing facilities.

Aircraft-based mass balance estimate of methane emissions from offshore gas facilities in the southern North Sea

Abstract. Atmospheric methane (CH4) concentrations have more than doubled since the beginning of the industrial age, making CH4 the second most important anthropogenic greenhouse gas after carbon dioxide (CO2). The oil and gas sector represents one of the major anthropogenic CH4 emitters as it is estimated to account for 22 % of global anthropogenic CH4 emissions. An airborne field campaign was conducted in April–May 2019 to study CH4 emissions from offshore gas facilities in the southern North Sea with the aim of deriving emission estimates using a top-down (measurement-led) approach. We present CH4 fluxes for six UK and five Dutch offshore platforms or platform complexes using the well-established mass balance flux method. We identify specific gas production emissions and emission processes (venting and fugitive or flaring and combustion) using observations of co-emitted ethane (C2H6) and CO2. We compare our top-down estimated fluxes with a ship-based top-down study in the Dutch sector and with bottom-up estimates from a globally gridded annual inventory, UK national annual point-source inventories, and operator-based reporting for individual Dutch facilities. In this study, we find that all the inventories, except for the operator-based facility-level reporting, underestimate measured emissions, with the largest discrepancy observed with the globally gridded inventory. Individual facility reporting, as available for Dutch sites for the specific survey date, shows better agreement with our measurement-based estimates. For all the sampled Dutch installations together, we find that our estimated flux of (122.9 ± 36.8) kg h−1 deviates by a factor of 0.64 (0.33–12) from reported values (192.8 kg h−1). Comparisons with aircraft observations in two other offshore regions (the Norwegian Sea and the Gulf of Mexico) show that measured, absolute facility-level emission rates agree with the general distribution found in other offshore basins despite different production types (oil, gas) and gas production rates, which vary by 2 orders of magnitude. Therefore, mitigation is warranted equally across geographies.

A neural network-based model for cross-border e-commerce supply chain demand forecasting and inventory optimization

Abstract The development of the Internet makes the e-commerce transaction scale in the total global trade share grow year by year, and cross-border e-commerce has become an important growth point of global trade by virtue of its unique advantages. In this paper, the ARIMA model is used to obtain the time series demand change of a cross-border e-commerce supply chain, and the results are input into the LSTM model to realize the construction of a cross-border e-commerce supply chain demand forecasting model. The ABC inventory classification method and economic lot ordering model are used as the basis for the establishment of cross-border e-commerce supply chain inventory control strategies and multi-cycle inventory control models. Taking the sales data of WT enterprise from May 2022 to May 2023 as an example, the effectiveness of the ARIMA-LSTM model in cross-border e-commerce supply chain demand forecasting is analyzed, and the inventory control optimization results of the multi-period inventory control model are verified. The relative error fluctuation range of supply chain demand forecasting of the ARIMA-LSTM model is between [-0.1,0.2], and the cross-border e-commerce supply chain’s monthly demand forecast MAPE value is only 0.0135. After using the inventory control optimization model, the annual average inventory is reduced by 178.42 tons, and the total cost of inventory is reduced by 0.09*108 yuan. Relying on neural networks can achieve accurate prediction of cross-border e-commerce supply chain demand and optimize cross-border e-commerce supply chain inventory.

KONSEP MANAJEMEN SARANA DAN PRASARANA PENDIDIKAN LEMBAGA ISLAM DI MTs S SUNGAI RAMBAH KABUPATEN PADANG PARIAMAN

This study is motivated by the need for planning the procurement of facilities and infrastructure, because the madrasah has submitted proposals to receive assistance but not all of them have provided the expected results, resulting in a less than ideal learning environment due to inadequate facilities and infrastructure. There is still room for improvement in all aspects of facilities and infrastructure management, from initial inventory to final disposal. Therefore, there is room for development in the system used to oversee the operation of infrastructure and facilities. The purpose of the study was to identify and describe the factors that influence the development of infrastructure and services in the Sungai Rambah mountain range of Padang Pariaman Regency, including factors that encourage and hinder development. The analytical scriptural method was used to conduct this qualitative research. Interviews, field notes, and archival research were used to collect this data. Quantitative data analysis methods were not used to present information obtained from words, pictures, and actions. Applying for government assistance to plan facilities and infrastructure, purchasing facilities and infrastructure in accordance with the planning of facilities and infrastructure, using and maintaining facilities and infrastructure in accordance with the needs of students, conducting an annual inventory according to the provisions of the data, and abolishing goods in a timely manner depend on c. Based on these findings, schools are encouraged to prioritise the placement of student facilities to ensure the highest level of satisfaction among its customers. Keywords: Facilities and Infrastructure Management, Islamic Education Institution

Contribution of the open ocean to the nutrient and phytoplankton inventory in a semi-enclosed coastal sea

Abstract. The semi-enclosed coastal seas serve as a transition zone between land and the open ocean, and their environments are therefore affected by both. The influences of land were noticed but that of the open ocean were usually neglected. The Seto Inland Sea (SIS), which is connected to the Pacific Ocean, is a typical representative of semi-enclosed seas. To quantitatively assess the inventory of nutrients originating from land and the open ocean, as well as their supported phytoplankton in the SIS, we developed a three-dimensional coupled hydrodynamic–biogeochemical model and embedded a tracking technique in it. Model results showed that the open ocean contributes 61 % and 46 % to the annual inventory of dissolved inorganic nitrogen (DIN) and phytoplankton in the SIS, respectively. This proportion has apparent spatial variations, being highest near the boundary with the open ocean, decreasing from there towards the interior area of the SIS, and being lowest in the nearshore areas. The open ocean imports 799 mol s−1 of DIN to the SIS, 25 % of which is consumed by biogeochemical processes and 75 % of which is delivered again to the open ocean. Such a large amount of oceanic nutrient input and its large contribution to the inventory of DIN and phytoplankton suggest the necessity of considering the impact of open-ocean variabilities in the management of the land loading of nutrients for the semi-enclosed seas.

Retrieval of Monsoon Landslide Timings With Sentinel‐1 Reveals the Effects of Earthquakes and Extreme Rainfall

AbstractMonsoon rainfall triggers hundreds of landslides across Nepal every year, causing significant hazard and mass wasting. Annual inventories of these landslides have been mapped using multi‐spectral satellite images, but these images are obscured by cloud cover during the monsoon, making it impossible to use them to constrain landslide timing. We employ recently developed techniques to derive individual timings from Sentinel‐1 for 579 landslides in 2015, 2017, 2018, and 2019 in Nepal. We use this new timing information alongside satellite rainfall data to identify spatio‐temporal clusters of landslides and associate these with periods of particularly intense rainfall. We also observed that during the 2015 monsoon, many landslides failed earlier and in dryer conditions than in 2017–2019. We use physical models to demonstrate how this requires a temporary loss of hillslope strength following the Mw 7.8 Gorkha Earthquake sequence and suggest a modeled cohesion loss in the range 1–3 kPa.

Local-to-regional methane emissions from the Upper Silesian Coal Basin (USCB) quantified using UAV-based atmospheric measurements

Abstract. Coal mining accounts for ∼12 % of the total anthropogenic methane (CH4) emissions worldwide. The Upper Silesian Coal Basin (USCB), Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coal bed CH4 emissions into the atmosphere are poorly characterized. As part of the carbon dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May–June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speed, and surface wind direction. We used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2=0.7–0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified. As an alternative, we have developed three upscaling approaches, i.e., by scaling the European Pollutant Release and Transfer Register (E-PRTR) annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate, which are derived from the hourly emission inventory. These estimates are in the range of 256–383 kt CH4 yr−1 for the inverse Gaussian (IG) approach and 228–339 kt CH4 yr−1 for the mass balance (MB) approach. We have also estimated the total CO2 emissions from coal mining ventilation shafts based on the observed ratio of CH4/CO2 and found that the estimated regional CO2 emissions are not a major source of CO2 in the USCB. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the u-net deep learning model

ABSTRACT Repeated temporal mapping of landslides is essential for investigating changes in landslide movements, legacy effects of the landslide triggering events, and susceptibility changes in the area. However, in order to perform such investigations, multi-temporal (MT) inventories of landslides are required. The traditional approach of visual interpretation from cloud-free optical remote sensing imageries is time consuming and expensive. Recent endeavors exploring Convolutional Neural Networks and deep learning models have made rapid and accurate mapping of landslides feasible but have not been applied for multi-temporal landslide mapping in the Himalayas, yet. Earlier models used a standard supervised learning approach, with a small landslide inventory over a limited area used for training, which is then utilized to predict landslides in nearby areas. We propose a new strategy, using geographically separate training samples to design a standard approach which can be utilized to create multi-temporal landslide inventories. RapidEye images of 5-meters spatial resolution are used to generate MT landslide inventories in the study area of Rasuwa district, Nepal. We test the effectiveness of the model by training with only 55 landslides and predicting for a different area. Then, using the weights attained from this first training phase, we use transfer learning to map landslides over a time period between 2013 and 2019 in the Rasuwa district attaining an average F1-score of 0.69 for the study area. We also perform a spatial comparison between the manual (observed) and predicted inventories to evaluate the differences between landslide densities and overall landslide statistics of landslide area distribution. The benefit of a transfer learning-based model training is that it circumvents the need for generating annual inventories for training a deep learning. A single event-based inventory is enough to generate landslide inventories over a number of years, at least until landslide conditioning factors do not change significantly. This application can enable automated workflows to generate MT landslide inventories of particular areas as the basis for landslide evolution and movement change analysis.

Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Rainfall-induced landslides cause frequent disruptions to critical infrastructure in mountainous countries. Climate change is altering rainfall patterns and localizing extreme rainfall events, increasing the occurrence of landslides. For planning climate-resilient critical infrastructure in landslide-prone regions, it is urgent to understand the changing landslide susceptibility in relation to changing rainfall extremes and spatially overlay them with critical infrastructure to determine risk zones. As such, areas requiring financial reinforcements can be prioritized. In this paper, we develop a framework linking changing rainfall extremes to landslide susceptibility and intensity of critical infrastructure — exemplified on a national scale using Nepal as a case study. First, we define a set of 21 different unique rainfall indices that describe extreme and localized rainfall. Second, we prepare a new annual (2016–2020) inventory of 107,900 landslides in Nepal mapped on PlanetScope satellite imagery. Next, we prepare a landslide susceptibility map by training a random forest model using the collected extreme rainfall indices and landslide locations in combination with spatial data on topography. Fourth, we construct a gridded critical infrastructure spatial density map that quantifies the intensity of infrastructure (i.e., transportation, energy, telecommunication, waste, water, health, and education) at each grid location using OpenStreetMap. The landslide susceptibility map classified Nepal's topography into low (36 %), medium (33 %), and (32 %) high rainfall-triggered landslide susceptibility zones. The landslide susceptibility map had an average area under the receiver characteristic curve value of 0.94. Finally, we overlay the landslide susceptibility map with the critical infrastructure intensity to identify areas needing financial reinforcement. Our framework reasonably mapped critical infrastructure hotspots in Nepal prone to landslides on a 1 km grid. The hotspots are mainly concentrated along major national highways and in provinces 4, 3, and 1, highlighting the need for improved land management practices. These hotspots need spatial prioritization regarding climate-resilient critical infrastructure financing and slope conservation policies. The research data, output maps, and code are publicly released via an ArcGIS WebApp and GitHub repository. The framework is scalable and can be used for developing infrastructure financing strategies for landslide mountain regions and countries.

Survey of U.S. cow-calf producer methods and opinions of cattle health and production record-keeping

The objective of this study was to describe U.S. cow-calf pro­ducer characteristics associated with the use of cattle health and production records (CHPR). We anonymously surveyed 14,294 cow-calf producer members of the National Cattle­men’s Beef Association (NCBA). Multivariable logistic regres­sion by manual forward variable selection was used to test demographic factors for association with CHPR-keeping out­comes. In total, 3,741 (26.2%) responses were received, with 3,641 (97.3%) respondents actively involved in cow-calf pro­duction. Of 3,624 respondents who said whether or not they maintained CHPR, 3,169 (87%) used some form of CHPR. Of 3,133 respondents who described their CHPR-keeping meth­ods, 1,966 (62.8%) used handwritten, and 1,167 (37.2%) used electronic CHPR. Annual cow inventory (96%), breeding dates (89.3%) and calving dates (88.1%) were the most commonly recorded CHPR. Among all respondents, understanding commercially available CHPR software (57.2%) was the most commonly identified CHPR-keeping challenge. Factors associated with using any form of CHPR were seed­stock production (OR = 5.1; compared to non-seedstock production), respondent age (≤ 54 years: OR = 2.5; 55-64 years: OR = 1.7; 65-74 years: OR = 1.3; compared to age ≥ 75 years), female respondents (OR = 1.7; compared to male), respondent’s primary income source is cow-calf op­eration (OR = 1.5; compared to not being pri­mary income source), and respondent having a post-graduate or professional degree (OR = 1.7; compared to high-school diploma or less). This study concluded that demo­graphic factors such as producer age, gender, education level and operational goals impact CHPR-keeping on U.S. cow-calf operations.

Tunnel measurements reveal significant reduction in traffic-related light-absorbing aerosol emissions in China

Light-absorbing aerosols (LAA), including black carbon (BC) and brown carbon (BrC), profoundly impact regional and global climate. Vehicle emission is an important source of LAA in urban areas, but real-world emission features of LAA from the urban vehicle fleet are not fully understood. This study evaluates traffic-related BC and BrC emission factors (EFs) and their vehicular emission inventories via road tunnel measurements in Tianjin, China, in 2017 and 2021. The distance-based and fuel-based EFs of BC for the mixed fleet were 1.05 ± 1.28 mg km−1 veh−1 and 0.057 ± 0.057 g (kg-fuel)−1, respectively, in 2021, with a dramatic decrease of 80.6 % compared to those in 2017. The BC EFs for gasoline vehicles (GVs, including traditional gasoline and hybrid vehicles) and diesel vehicles (DVs) were 0.55 ± 0.065 mg km−1 veh−1 and 10.5 ± 2.52 mg km−1 veh−1, respectively, in 2021. Compared to 2017, the BrC EFs also decreased significantly in 2021, by 10.8–53.6 %, with 0.32 ± 0.45 mg km−1 veh−1 and 0.018 ± 0.020 g (kg-fuel)−1 of distance-based and fuel-based EFs for mixed fleet. The BrC EFs for GVs and DVs were 0.091 ± 0.024 mg km−1 veh−1 and 3.06 ± 0.91 mg km−1 veh−1, respectively, in 2021. Based on the BC and BrC EFs for GVs and DVs and annual mileage for each vehicle category, the annual vehicular LAA emission inventories were estimated. From 2017 to 2021, the annual vehicular LAA emissions in Tianjin have been significantly reduced, by 69 % for BC and 10 % for BrC. DVs account for a small amount of the vehicle population (8.4 %), but contribute to most of the BC (83 %) and BrC (86 %). Our study demonstrates the significant reduction of vehicular light-absorbing aerosols emission due to vehicle pollution prevention and control in China.

Forests at the fringe: Comparing observed change to projected climate change impacts for five tree species in the Pacific Northwest, United States

Rapid climate change over the coming century will impact suitable habitat for many tree species. In response to these changes in climate, areas that become unsuitable will see higher mortality and lower growth and recruitment. Therefore, early detection of demographic trends is critical for effective forest management. Recent 10-year remeasurement data from the United States (US) Department of Agriculture (USDA) Forest Service’s Forest Inventory and Analysis (FIA) Program’s national annual inventory of forest land provides an ideal data set for analyzing such trends over large areas. However, failure to distinguish between areas of future habitat contraction and expansion or persistence when estimating demographic trends may mask species’ shifts. We used remeasurement data to compare observed tree demographic rates with projected impacts of climate change for five important tree species in the Pacific Northwest. Projected impacts were based on spatial-Bayesian hierarchical models of species distributions, which were used to project areas where habitat would persist (remain climatically suitable), expand (become suitable), or contract (become unsuitable) under four future climate scenarios for the 2080s. We compared estimates of mortality and net-growth between these areas of shifting suitability and a naïve division of habitat based on elevation and latitude. Within these regions, we assessed the sustainability of mortality and determined that observational data suggest that climate change impacts were already being felt in some areas by some species. While there is an extensive literature on bioclimatic species distribution models, this work demonstrates they can be adapted to the practical problem of detecting early climate-related trends using national forest inventory data. Of the species examined, California black oak (Quercus kelloggii) had the most notable instances of observed data suggesting population declines in the core of its current range.

Assessment of pollutant emission in Poland from various categories of transport

Abstract This paper refers to the inventory of pollutant emissions in Poland from different transport categories over the period 1990–2020. The changes in the annual national emissions of selected pollutants, i.e. nitrogen oxides, non-methane volatile organic compounds, sulfur oxides, PM2.5, PM10, total suspended particulate matter, carbon monoxide, and lead were analysed. The shares of the national annual emissions of the examined pollutants from each studied transport category in the national annual emission from total transport were assessed. The sensitivity of the national annual pollutant emission inventory with regard to the transport categories and emitted substances under the study was assessed. The trend of decrease in the national annual emissions of the substances analysed was appraised as positive in the face of a substantial intensification of transport activities. Taking into account the fact that road transportation is responsible for a dominant share of the national emission of pollutants, important progress in the reduction of emissions is attributable to the substantial technical improvement of the internal combustion engines of road vehicles.