Climatic Zones Research Articles

Assessment of Fine-Scale Urban Heat Health Risk and Its Potential Driving Factors Based on Local Climate Zones in Shenzhen, China

Cities are facing increased heat-related health risks (HHRs) due to the combined effects of global warming and rapid urbanization. However, few studies have focused on HHR assessment based on fine-scale information. Moreover, most studies only analyze spatial HHR patterns and do not explore the potential driving factors. In this study, we estimated the potential HHRs based on the “hazard–exposure–vulnerability” framework by using multisource data, including the modified thermal–humidity index (MTHI), population density, and land cover. Then, the variations in the HHRs among different local climate zones (LCZs) at the fine spatial scale were analyzed in detail. Finally, we compared the different contributions of the LCZs and types of land cover to the HHRs and their three components by using multiple linear regression models. The results indicate that the spatial pattern of the HHRs was different from those of the individual components, and high-hazard regions do not mean high HHRs. There were huge variations in the HHRs among the different LCZs. The built-up LCZs typically had much higher HHRs than the natural ones, with compact LCZs facing the most severe risk. LCZ 6 (open low-rise buildings) had a relatively low HHR and should be paid more attention in future urban planning. Compared to the LCZs, the land covers better explained the variations in the HHR. In contrast, the LCZs better predicted the land surface temperatures. However, both the LCZs and land covers made only slight contributions to the heat exposure and vulnerability. Furthermore, the manmade buildings and impervious surface areas contributed much more to the HHR than the natural land covers. Therefore, the arrangement of the warming LCZs and land cover types is worthy of further investigation from the perspective of HHR mitigation.

Holocene palaeoecological changes in a transitional climate zone of western-central Mexico: The pollen and non-pollen palynomorphs record

The Alberca de Tacámbaro (AT) is located at a transitional zone between tropical and temperate climates at the core of the North American Monsoon area. This location features a mosaic vegetation comprising pine-oak forests and tropical dry forests. Currently there is no information regarding the dynamics or evolution during the Holocene of these vegetation types. The palynological record of the last 9400 years BP preserved in the lacustrine sequence of the AT was used to document changes in the composition of terrestrial and aquatic communities, evenness, and rate of change. Pinus and Quercus forests dominated the terrestrial palynological record, but mesophytic forests were also recorded throughout the sequence. Four tropical dry forest taxa assemblage expansions were identified at 9200 to 8420 yr BP; 5900 to 5230 yr BP; 3100 to 2800 yr BP; and 2300 to 2000 yr BP. These phases correlated with times of reduced North American Monsoon intensity, suggesting that these communities thrived during drought periods. Variations in non-pollen palynomorphs were synchronic with the lithostratigraphic, geochemical data as well as with the terrestrial changes. During the Meghalayan, compositional shifts in the vegetation and increases in the herbaceous elements indicated human impact, while aquatic taxa, suggested eutrophic and warmer conditions in the lake. By examining the history of this vegetation mosaic, the data on temporal vegetation dynamics during the Holocene offered clues about the response to ongoing global warming and how climate change will likely shape plant communities in western-central Mexico.

Architectural design strategies based on climatic zoning and thermal comfort indices (Case study: Qazvin)

Architectural design strategies based on climatic zoning and thermal comfort indices (Case study: Qazvin)

What explains rice exports? An analysis of major rice‐exporting countries

AbstractThis study examines the drivers of rice trade. The analysis uses the standard comparative advantage model, the Heckscher–Ohlin–Vanek (HOV) framework, supplemented with a gravity‐type equation. Using the Poisson pseudo‐maximum likelihood (PPML) estimation for data from 2002 to 2020, the analysis broadly confirms HOV model predictions. Results indicate that arable land, along with GDP, distance, precipitation and crop season temperature, significantly influences rice trade dynamics. The results showed that the precipitation play a key role in influencing the rice trade rather than the blue water availability. However, agricultural water stress discouraged exports and encouraged imports.

DNA-metabarcoding of cyanobacteria and microalgae in chernozem soils of temperate continental climate of the forest-steppe zone of Eurasia under different degrees of agrotechnology intensification.

Chernozem soil is a valuable resource and contains a great diversity of microorganisms that play a global role in the process of soil formation, the species diversity of which has changed over the last five years under the influence of different agrotechnologies. For the first time, under the conditions of the Central Chernozem region, grain and fallow crop rotation, studies using the DNA-metabarcoding method were carried out to study the taxonomic structure of bacteria, fungi, cyanobacteria, and microalgae communities in the arable horizon of typical medium loamy chernozem under winter wheat cultivation. A comparative analysis of the composition of the genotypes showed significant differences in the presented level of mineral nutrition of the soil NPK (60) and NPK (100) compared with the control variant. After processing the 16S and 18S rRNA datasets, a similar trend of decreasing numbers of pro- and eukaryotic species was found from 6296 (control without MF) to 5310 with NPK (60) and to 4643 with NPK (100), respectively. The Chao1 index indicated that the expected diversity within the prokaryotic group was higher in the control without MF at 211, but decreased to 182 and 193 with NPK (60) and NPK (100) fertilizers, respectively. Analysis of the eukaryotic group revealed a 2.6- and 2.9-fold decrease in diversity by class and genus, respectively, depending on the nutritional levels in agrotechnologies, owing to the use of MF. In the prokaryotic community, Alphaproteobacteria microorganisms predominated at an amount of 14.20-14.46%, with Cyanophyceae accounting for 5.2-9.9%. The diversity of eukaryotes was smaller than the number of classes of prokaryotes; the main dominant were Zygnematophyceae 19.5-41%, Chlorophyceae occupied 10.4-15.8%. On the other hand, the doses of fertilizers used contributed to the emergence of dominant species adapted to high doses of mineral nutrients for plants.

Projection of climate change impact on main climate variables and assessment of the future of Köppen–Geiger climate classification in Iran

AbstractHuman society and the environment are facing significant challenges due to climate change. Climate change is projected to impact main climate variables, such as temperature and precipitation. Changes in main climate variables affect climate classification and alter climate zone maps. In this research, first, the projection of temperature and precipitation in 30 main stations of Iran under the Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from Coupled Model Intercomparison Project Phase Six (CMIP6) for the end of the twenty-first century (2071–2099) was carried out. Then, the future of climate zone maps was assessed in Iran by Köppen-Geiger climate classification. The evaluation of the model data based on observation data for the period of 1991–2020 showed an acceptable correlation, with R-square and RMSE values in the ranges of 0.67–0.96 and 2.44–8.38, respectively. Results showed that the temperature in the future period (2071–2099) will increase by 1–4.7 °C under all scenarios compared to the historical study period (1991–2020), while the precipitation will either increase or decrease depending on the season and the specific climate change scenario. Assessment of future climate classifications revealed that the BW (arid desert) and BS (semi-arid steppe) categories will increase, as classified by Köppen-Geiger, will increase. At the same time, Ds and Cs (dry summer) classifications will decrease in during the study period over Iran. These findings provide policymakers with some insights into how to deal with the impacts of climate change in the future and implement some measures now.

Analysis of seasonal rainfall variability with innovative graphical methods of Konya Closed Basin, Türkiye

This study looked into the variability of seasonal rainfall in the semi-arid Konya Closed Basin (KCB), a water-limited region with high exploitation demand for water resources due to agricultural activities. In the study, seasonal precipitation data of eleven stations in the basin for the period 1971–2020 were used and analyzed with four trend methods (Şen-ITA, CWTSD, IPTA, and TPSC). Visual and statistical findings from the analyses showed generally increasing trends in winter and summer precipitation and decreasing trends in spring precipitation in the basin. In autumn precipitation, according to Şen-ITA, there is an increasing trend in seven stations and a decreasing trend in four stations, while according to CWTSD, there is an increasing trend in six stations and a decreasing trend in five stations. Based on the arithmetic mean IPTA graphs, Winter-Spring transitions in all stations occur from a rising trend region to a falling trend region, but Spring-Summer transitions take the reverse direction. In the standard deviation IPTA graphs, similar transitions in the trend regions are predominant. To the mean and standard deviation TPSC graphs, most of the stations generally have similar behavior. Spring-Summer transition arrows are dominant in Zone III, while Summer-Autumn and Autumn-Winter transitions are dominant in Zone I. Although Winter-Spring transitions are variable, they are more intense in Zone I and III. CWTSD and Şen-ITA methods are also consistent with the results obtained from IPTA graphs, both visually and numerically.

Detection and Attribution of Changes in Precipitation Extremes in China and Its Different Climate Zones

Abstract Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales. Significance Statement The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.

Monthly rainfall prediction for different climatic zones in south Africa for 2024 using a random forest model

This study predicted 2024 rainfall in different climatic zones in South Africa using a random forest model. Previous studies have shown that random forests performed better than other models for rainfall prediction. South Africa was divided into nine using the Koppen-Geiger climate classification system, and three cities were selected for each climatic zone. Atmospheric datasets from the South African Weather Service and the National Aeronautics and Space Agency were used for this study. The datasets were trained, tested, and validated to assess the model's accuracy. With good forecast ability observed, the random forest was then used for monthly rainfall for 2024. The result of this prediction was then compared with 2022 and 2023 rainfall. The result indicated months where much rain should be expected in various cities and cities that may likely experience droughts. This result is particularly important for agriculture, water resource management, and early drought/flooding warning systems.

Weather events influence survival and recruitment of Coereba flaveola (Bananaquit) in the Caribbean

Abstract The West Indies is considered a biodiversity hotspot and a priority for ecological conservation efforts. Understanding how environmental conditions influence survival of resident avifauna is an important information need given the predicted increases of drought and frequency and intensity of severe storms in the region. Throughout much of the Caribbean, Coereba flaveola (Bananaquit) are widespread and abundant, traits that may facilitate understanding their response to environmental changes in the region. We used a 10-yr capture–mark–recapture data set to examine C. flaveola survival, recruitment, population growth, and age structure in context of monthly and seasonal precipitation and temperatures, drought conditions, and occurrence and intensity of storm events. Our models suggested wing length, occurrence of storms, and drought all influenced survival. Both the incidence of storms and drier than average conditions in the preceding wet season (April–June) decreased survival. Sex of bird and net hours influenced capture probability, but weather influences on recruitment were equivocal. During non-storm years, mean population growth was stable at 1.019 (95% CI: 0.962, 1.098), but dropped to 0.843 (95% CI: 0.795, 0.846) in storm years. Increasing frequency of storms, such as back-to-back years, would likely push the growth rate lower. A protracted pattern of increased storm frequency, especially if coupled with a subsequent drought during the wet season, may lead to localized extirpations or strongly reduced populations.

Impact of green space patterns on PM2.5 levels: A local climate zone perspective

Green spaces have been demonstrated to significantly decrease PM2.5 levels. However, the impact of green spaces on PM2.5 levels in different spatial forms of urban blocks is not yet fully understood. This research utilized ensemble machine learning algorithms to investigate the impact of green space spatial patterns, assessed through landscape pattern analysis, to PM2.5 concentrations during summer and winter based on local climate zones (LCZ). The results revealed significant differences in PM2.5 concentration across the seven types of primary LCZs, primarily influenced by meteorological factors. In winter, eight green space indicators exhibited a more substantial contribution to PM2.5 levels in comparison to the summer season. Significant discrepancies were noted in the contributions of these indicators across different LCZs. Patch density (PD) and landscape shape index (LSI) made a more substantial contribution, while relative patch richness (RPR) and Shannon's evenness index (SHEI) showed a less significant contribution. The spatial pattern of green spaces was significantly related to their contributions. Among the seven predominant LCZs, the five key indicators included PD, LSI, Shannon's diversity index (SHDI), area-weighted mean patch area (AREA_AM), and connectance index (CONNECT). Spatial heterogeneity was further observed in the positive and negative contributions of each green space indicator. This study enhances understanding of the impact of green spaces on PM2.5 across LCZs and provides valuable insights for urban management and planning.

Isolation and characterization of mung bean (Vigna radiata L.) rhizobia in Myanmar

AbstractWe collected soil samples from six major mung bean cropping regions in Myanmar: Sagaing, Mandalay, Nay Pyi Taw, and Magway in the tropical savanna climate zone and Bago and Yangon in the tropical monsoon climate zone. All fields grew mung bean for at least 5 years and had no history of rhizobial inoculation. Mung bean ‘Yezin-11’, a popular cultivar in Myanmar, was inoculated with soil suspensions. From the nodules formed on the roots, we isolated 55 rhizobial strains. Identification of the isolates revealed the dominant species of indigenous rhizobia in each region. We identified 53 Bradyrhizobium strains and 2 Ensifer strains. Bradyrhizobium yuanmingense was dominant in the tropical savanna zone and Bradyrhizobium sp. (B. liaoningense or B. diversitatis) and B. centrosematis were dominant in the tropical monsoon zone. Principal component analysis indicates that the dominance of B. yuanmingense in the tropical savanna zone might be due to high concentration of NO3-N and P2O5 in the soil. It also indicates that the dominance of B. centrosematis in the tropical monsoon zone might be caused by drastically low pH and high concentration of NH4 in the soil. Bradyrhizobium centrosematis YGN-M9, B. yuanmingense SGG-M3, and Bradyrhizobium sp. BGO-M5 significantly increased nodulation (nodule number and nodule dry weight), acetylene reduction activity, and shoot dry weight, respectively, relative to Ensifer terangae MDY-M6. Co-inoculation with these three strains increased nodulation significantly compared with single inoculation of BGO-M5. The characterization of mung bean rhizobia and selection of microbial inoculant candidates will be useful for the development of microbial inoculants in Myanmar.

Development of near-optimal advanced control sequences for chiller plants with water-side economizers in U.S. Climates (ASHRAE RP-1661)

Various advanced control sequences for chiller plants with water-side economizers (WSE) have been proposed in literature, but the evaluation and optimization of those controls is limited. It is possible to maximize energy savings by selecting different sequences and related parameters based on the plant configuration, load, and climate. This paper addresses this gap by developing near-optimal advanced control sequences for chiller plants with WSEs. First, advanced control sequences for chiller plants with WSEs are categorized into condenser water, chilled water, and hybrid controls and representative sequences from each category are identified. Next, 504 different scenarios are optimized. These scenarios represent all possible combinations of two plant configurations, a constant or variable load profile, three advanced control sequences, and seven optimization parameter combinations in six climate zones. The results show the recommended near-optimal sequences can reduce energy consumption by up to 15% relative to the baseline depending on the configuration, load profile, and climate. Specifically, the CW-CHW sequence is recommended for the majority of systems because it is often the most energy efficient and/or reduces the runtime of chillers. The methodology in this paper provides practical guidance for achieving energy savings through near-optimal control of chiller plants with WSEs.

Anomalous latitudinal gradients in parasitoid wasp diversity-Hotspots in regions with larger temperature range.

Knowledge of global patterns of genetic diversity is essential for biodiversity conservation as this parameter describes the ability of a species to respond to environmental changes. Ichneumonoids parasitoid wasps are among the few taxa showing an anomalous latitudinal diversity gradient. Using the largest georeferenced molecular dataset for this group, we used a macrogenetics approach to examine latitudinal patterns and predictors of intraspecific genetic diversity. We calculated the mean nucleotide diversity of mitochondrial DNA barcode sequences at three geographic levels: grid cells, latitudinal bands and climatic zones. Nucleotide diversity values were consistently higher at northern temperate latitudes, peaking at 50°. We found a positive but weak relationship between intraspecific diversity and the latitude, between intra- and interspecific diversity, and a positive effect of the temperature range. Examining the spatial relationship between different levels of biodiversity and its drivers is particularly relevant considering climate change and its impact on species distribution. Yet, in insects, it has been challenging to integrate ecological, evolutionary and geographical components when analysing the processes leading to species richness gradients.

Traffic Intensity as a Factor Influencing Microplastic and Tire Wear Particle Pollution in Snow Accumulated on Urban Roads

Traffic-related roads are an underestimated source of synthetic particles in the environment. This study investigated the impact of traffic volume on microplastic (MP) and tire wear particle (TWP) pollution in road snow. An examination was conducted in a medium-sized city situated in northeastern Poland, known for being one of the cleanest regions in the country. MPs and TWPs were found at all 54 sites, regardless of the intensity of traffic. The average concentration for all samples was 354.72 pcs/L. Statistically significant differences were found between the average values of the particle concentration on low, medium, and heavy traffic roads, amounting to 62.32 pcs/L, 335.97 pcs/L, and 792.76 pcs/L, respectively. Within all three studied groups of roads, MPs and TWPs with the smallest size, ranging from 50 to 200 μm, were prevalent. In all of the studied groups of roads, four analyzed shapes of particles were found, with irregular fragments being the most abundant form (89.23%). The most frequently recorded color among the collected samples was black (99.85%), and the least frequently recorded color was blue, constituting only 0.01%. This study suggests that snow cover on the roads may act like a temporary storage of pollutants during winter particularly in the temperate climate zone and, after thawing can significantly increase the concentration of MPs and TWPs in surface waters. Possible measures to decrease the release of MPs and TWPs into the environment in the city may include reducing the traffic volume and speed, implementing street sweeping, utilizing filtration chambers, and installing stormwater bioretention systems or settling ponds.

Impact of vegetation coverage and configuration on urban temperatures: a comparative study of 31 provincial capital cities in China

Urban vegetation plays a crucial role in regulating temperatures and heat waves in urban areas. However, the influence of vegetation coverage and its configuration on surface temperatures in different climate zones at a national scale is unclear. To address this, we utilized high-resolution data to detect spatial patterns for 31 provincial capital cities in China. We integrated day and night surface temperatures to determine the influence of vegetative coverage and configuration on urban temperatures across different climate zones and city sizes. Our study revealed that a subtropical monsoon climate and medium-sized cities had the highest vegetative coverage and shape complexity. The best connectivity and agglomeration of vegetation were found in a temperate monsoon climate and large cities. In contrast, small cities, especially those under a temperate continental climate, had low vegetation coverage, high fragmentation, and weak agglomeration and connectivity. In addition, vegetative coverage had a negative impact on daytime surface temperatures, especially in large cities in a subtropical monsoon climate. However, an increase in vegetation coverage could result in warming at night in small cities in temperate continental climates. Although urban vegetation configuration also contributed to moderating surface temperatures, especially at night, they did not surpass the influence of vegetation coverage. The effect on nighttime temperatures of the configuration of vegetation increased by 3–6% relative to that of daytime temperatures, especially in large cities in a temperate monsoon climate. The contribution vegetation coverage and configuration interaction to cooling efficiency decreased at night, especially in medium-sized cities in a temperate continental climate by 3–5%. In addition, this study identified several moderating effects of natural and social factors on the relationship between urban vegetation coverage and surface temperatures. High duration of sunshine, low humidity and high wind speed significantly enhanced the negative impact of vegetation coverage on surface temperatures. In addition, the moderating effect of vegetation coverage was more pronounced in low population density cities and high gross domestic product. This study enhances understanding of the ecological functions of urban vegetation and provides a valuable scientific basis and strategic recommendations for optimizing urban vegetation and improving urban environmental quality.

Correction: Cool season precipitation projections for California and the Western United States in NA-CORDEX models

Correction: Cool season precipitation projections for California and the Western United States in NA-CORDEX models

Detecting Drought-Related Temporal Effects on Global Net Primary Productivity

Drought has extensive, far-reaching, and long-lasting asymmetric effects on vegetation growth worldwide in the context of global warming. However, to date, few scholars have attempted the systematic quantification of the temporal effects of drought on global vegetation across various vegetation types and diverse climate zones. Addressing this gap, we quantitatively investigated the effects of drought on global vegetation growth under various scenarios, considering lagged and cumulative effects as well as combined effects in the 1982–2018 period. Our investigation was based on long-term net primary productivity (NPP) and two multiple-timescale drought indices: the standardised precipitation index (SPI) and the standardised precipitation and evapotranspiration index (SPEI). Our main findings were the following: (1) SPI and SPEI exhibited lagged effects on 52.08% and 37.05% of global vegetation, leading to average time lags of 2.48 months and 1.76 months, respectively. The cumulative effects of SPI and SPEI were observed in 80.01% and 72.16% of global vegetated areas, respectively, being associated with relatively longer cumulative timescales of 5.60 months and 5.16 months, respectively. (2) Compared to the scenario excluding temporal effects, there were increases in the explanatory powers of SPI and SPEI for variations in vegetation NPP based on the lagged, cumulative, and combined effects of drought: SPI increased by 0.82%, 6.65%, and 6.92%, respectively, whereas SPEI increased by 0.67%, 5.73%, and 6.07%, respectively. The cumulative effects of drought on global vegetation NPP were stronger than the lagged effects in approximately two-thirds (64.95% and 63.52% for SPI and SPEI, respectively) of global vegetated areas. (3) The effects of drought on vegetation NPP varied according to climate zones and vegetation types. Interestingly, vegetation in arid zones was the most sensitive and resilient to drought, as indicated by its rapid response to drought and the longest cumulative timescales. The vegetation NPP in tropical and temperate zones exhibited a relatively stronger response to drought than that in cold and polar zones. The strongest correlation of vegetation NPP with drought occurred in shrubland areas, followed by grassland, cropland, forest, and tundra areas. Moreover, for each vegetation type, the correlations between vegetation NPP and drought differed significantly among most climate zones. (4) The vegetation NPP in warming-induced drought regions displayed a higher correlation to drought than that in non-warming-induced drought regions, with shorter lagged and longer cumulative timescales. Our findings highlight the heterogeneity of the lagged, cumulative, and combined effects of drought across various climate zones and vegetation types; this could enhance our understanding of the coupling relationship between drought and global vegetation.

A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network

Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial resolution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983–2100 in a high spatial resolution using the LAI Downscaling Network (LAIDN) model driven by inputs including air temperature, relative humidity, precipitation, and topography data. The dataset spans the historical period (1983–2014) and future scenarios (2015–2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indicating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environmental sciences across present and future periods.

Greenhouse gas emission from prescribed fires is influenced by vegetation types in West African Savannas

Greenhouse gas (GHG) emissions from prescribed fires are poorly investigated, resulting in a high uncertainty in GHG budgets. Using, a carbon mass balance approach and experimental prescribed fires in 80 plots, this study assessed carbon emissions and established emission factors (EFs) for carbon dioxides (CO2), carbon monoxide (CO), and methane (CH4) across climate zones and vegetation types. In grass and shrub savannas, fires could burn intensely due to the lower moisture content and continuous spatial distribution of biomass fuel, causing greater carbon emissions with 1.61 ± 0.13 t C ha−1 and 1.01 ± 0.13 t C ha−1, respectively. Despite their low carbon emissions, tree savannas (1658.17 ± 11.13 g kg−1) and woodlands (1629.94 ± 12.23 g kg−1) have the highest EFs, which can be attribute to the high carbon content of biomass fuel in these vegetation types. Vegetation types and their interaction with climate zones have a substantial impact on carbon emissions and carbon species EFs, and should therefore be considered in assessing GHG emissions from fires. The findings from this study provide a useful basis for improving the national measurement, reporting, and verification of GHG emissions and for improving the measurement of the global balance of GHG emissions from fires.