Systematic Datasets Research Articles

Floods in the Pyrenees: a global view through a regional database

Abstract. This paper shows the first systematic dataset of flood episodes referring to the entire Pyrenees massif, named PIRAGUA_flood, which covers the period 1981–2015 (available at http://hdl.handle.net/10261/270351, last access: 21 July 2024). First, the structure of the database is detailed so that it can be reproduced anywhere else in the world, adapting to the specific nature of each situation. Subsequently, the paper addresses the spatial and temporal distribution of flood episodes and events (including trends) that affected the Pyrenees regions of Spain (Catalonia, Aragon, Navarre, the Basque Country), France (Nouvelle-Aquitaine, Occitanie) and Andorra, as well as the massif as a whole, for a given period of time. In the case of the Spanish regions, it was also possible to analyse the compensation payments by the Spanish Insurance Compensation Consortium and the number of deceased. The weather types associated with flood episodes were also classified based on sea level pressure and 500 hPa geopotential height from ERA5. The results show 181 flood events and 154 fatalities, some of which affected more than one region. In the Spanish part of the Pyrenees, between 1996 and 2015, there was a total compensation payout amounting to EUR 142.5 million (2015). The eastern part of the area records more flood events than the western one, with Catalonia being the community that registered the highest number of events, followed by Andorra and Occitanie. Associated weather types are dominated by the southern component flow over the Pyrenees region, with a talweg on the Iberian Peninsula and a depression in the vicinity, either in the Atlantic or in the Mediterranean. In terms of the entire massif, there is a slight positive trend of 0.84 events per decade, driven by the evolution of ordinary and extraordinary floods but not significant at 95 %. At a regional level, flood behaviour is more heterogeneous, although not significant for the most part. Nouvelle-Aquitaine is the only region that shows a positive and significant trend of 0.34 events per decade.

Reconstructing 120 years of climate change impacts on Joshua tree flowering.

Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records and reveals a slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years-but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.

A treasure trove of 1034 actinomycete genomes.

Filamentous Actinobacteria, recently renamed Actinomycetia, are the most prolific source of microbial bioactive natural products. Studies on biosynthetic gene clusters benefit from or require chromosome-level assemblies. Here, we provide DNA sequences from >1000 isolates: 881 complete genomes and 153 near-complete genomes, representing 28 genera and 389 species, including 244 likely novel species. All genomes are from filamentous isolates of the class Actinomycetia from the NBC culture collection. The largest genus is Streptomyces with 886 genomes including 742 complete assemblies. We use this data to show that analysis of complete genomes can bring biological understanding not previously derived from more fragmented sequences or less systematic datasets. We document the central and structured location of core genes and distal location of specialized metabolite biosynthetic gene clusters and duplicate core genes on the linear Streptomyces chromosome, and analyze the content and length of the terminal inverted repeats which are characteristic for Streptomyces. We then analyze the diversity of trans-AT polyketide synthase biosynthetic gene clusters, which encodes the machinery of a biotechnologically highly interesting compound class. These insights have both ecological and biotechnological implications in understanding the importance of high quality genomic resources and the complex role synteny plays in Actinomycetia biology.

CHORUS: A New Dataset of State Interest Group Policy Positions in the United States

Abstract Research on the activities and influence of interest groups in state legislatures faces a data problem: we are missing a comprehensive, systematic dataset of interest groups’ policy preferences on state legislation. We address this gap by introducing the Dataset on Policy Choice and Organizational Representation in the United States (CHORUS). This dataset compiles over 13 million policy positions stated by tens of thousands of interest groups and individuals on bills in 17 state legislatures over the past 25 years. We describe the process used to construct CHORUS and present a new network science technique for analyzing policy position data from interest groups: the layered stochastic block model, which groups similar interest groups and bills together, respectively, based on patterns in the policy positions. Through two demonstrative applications, we show the utility of these data, combined with our novel analytical approach, for understanding interest group configurations in different state legislatures and policy areas.

Design and Implementation of a Physical Education Teaching and Training Mode Management System

With the advent of the smart age, all industries are undergoing revolutionary changes, and more reliable, faster, and unmanned data analysis and mining have become a prominent feature of today's industry. With basketball as an iconic sport, better data processing models will drive sports games to be played more efficiently. As an essential sport in China, basketball has more than 100 years of history. With the leap development of basketball, data analysis technology has also been widely used. The data seen in basketball games is a set of systematic data formed by professional workers through division of labor and close cooperation, classification and aggregation, and statistical analysis. In addition, based on the information on the court, spectators get a general idea of how the players perform in the Game and predict where the data will go. This article explores the significance and application of basketball game data analysis methods and shows that the analysis of basketball game data can effectively promote the development of basketball.

Изучение спектрометрических особенностей лесных семян для улучшения посевных качеств: ретроспективный кластерный анализ направлений научного ландшафта

Forest seeds spectral data in the visible and infrared regions of electromagnetic radiation lengths quite effectively
 differentiate the origin, viability, types of seeds, their infestation with pests and diseases, the ability to absorb and lose
 water. The search for a method of seed testing that is both experimentally simple, fast and effective for predicting germination is necessary to increase the energy efficiency of forest nurseries in the production of planting material. The retrospective references systematization (N = 55, 1998-2023, terms [Scholar Query = seeds* AND (spectr* OR optic*) (properties OR features) AND analysis]) into clusters was carried out on the basis of eight performance criteria represented by
 rank variables. The level of similarity and difference between clusters is determined by the method of the most distant
 neighbors with the grouping of data by the square of the Euclidean distance. The most distant criterion from other criteria
 is the level of invasiveness of testing (the square of the Euclidean distance is 25, p < 0.05). Correlation analysis of nonparametric criteria indicates a direct strong interaction between the level of financial and organizational costs (Spearman
 coefficient ρ = 0.77; p = 0.0008), time costs and low machine learning capability (ρ = 0.725; p = 0.0008). In the future, it
 is planned to periodically supplement the set of systematic data to obtain an objective assessment of seed testing methods,
 as well as using a seed passport to evaluate the relationship of RGB spectral data of more than 1 000 individual seeds
 with early growth of seedlings in a post-pyrogenic experimental site of the forest landscape of the Voronezh region by
 example (Pinus sylvestris L. var. Negorelskaya).

Multi-element geochemical analyses on ultrafine soils in Western Australia – towards establishing abundance ranges in mineral exploration settings

In this study, we present summary statistics for multi-element soil geochemistry across Western Australia based on over 74 000 soil samples using the UltraFine+ ® method that extracts and analyses the clay (<2 μ m) fraction of a soil sample. This method is a critical advancement for the detection of mobile element signatures for soil geochemical mineral exploration surveys in cover. However, existing estimates of background metal abundances acquired with other methods and on different sample media do not readily provide context for these analyses, as recovery from the fine fraction differs to that of whole-sample analysis. We therefore present herein the geochemical results for 52 elements, including precious, base and critical metals, as well as commonly associated pathfinder elements for Western Australian samples analysed during several research projects by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). This dataset is separated by tectonic unit into the Eastern Goldfields Superterrane and the Youanmi Terrane in the Yilgarn Craton, the Pilbara Craton and Sylvania Inlier, the Gascoyne, Lamboo and Aileron Provinces, and the Bryah and Earaheedy Basins to provide exploration-relevant context in these areas. We discuss some of the general trends observed for 12 of these elements, as well as some considerations for the use of these data in comparison to other geochemical soil surveys and with regards to mineral exploration settings. The samples presented in this study are not evenly distributed across Western Australia and limited information is available to correlate whether lithology at depth is mineralized or barren. However, in the absence of other systematic datasets using the <2 μ m-size fraction, these data present a suitable first-pass resource of element abundance ranges in areas of mineral exploration interest using the UltraFine+ ® method in some of the mineral-endowed areas of Western Australia. Supplementary material: Supplementary data A, UltraFine+ ® Soil Sampling Guide; B, UltraFine+ ® analyses results for available samples in Western Australia; C, Summary statistics of UltraFine+ ® analyses by simplified tectonic units; Supplementary Figure 1, Comparison of elemental mean values and uncertainties for ultrafine reference materials QC 320 and QC 422; Supplementary Figure 2, Violin plots of Ni, Cd, Mg and Au by simplified tectonic unit; Supplementary Figure 3, Location of soil samples in the Eastern Goldfields Superterrane analysed via UltraFine+ ® , and those collected as part of the NGSA and GSWA regolith programme; Supplementary Figure 4, Comparison of W, Al, Mg and Ca results from UltraFine+ ® , NGSA and GSWA analyses in the Eastern Goldfields Superterrane are available at https://doi.org/10.6084/m9.figshare.c.6919933

Corpus-Oriented Affective Expressions in Spoken English

Abstract This research delves into the impact of affective expressions in spoken English, aiming to enhance spoken language teaching through corpus analysis. Recognizing English’s status as a global lingua franca, this study emphasizes the pivotal role of emotion in communication. By constructing a comprehensive spoken corpus, we uncover patterns in affective expressions to inform teaching strategies, thereby boosting oral competencies. Our methodology combines quantitative and qualitative approaches, analyzing 56,253.86 minutes of speech to create a diverse and systematic dataset. Results reveal a strong link between affective expression use and improved speaking skills, with users showing a notable accuracy increase in tests. Furthermore, implementing a multimodal teaching approach significantly alleviated speaking anxiety among learners. These findings underscore the importance of emotional expressions in enhancing communicative effectiveness and reducing language-related anxiety, offering significant implications for English education.

Access and use of immunoglobulins in supportive cancer care: A thematic analysis of a systematic review data set.

Secondary immunodeficiency (SID) disorders are known to occur in patients with haematological malignancies (HM) due to immunosuppressive treatments. Recurring infections causing subsequent morbidity and mortality commonly occur in this patient cohort. Immunoglobulin replacement therapy (IgRT) benefits patients with primary antibody deficiencies. However, evidence supporting their therapeutic role is not as explicit in SID-associated antibody deficiencies, which raises the questions regarding its use in SID and the knock-on effects of this use on its access and availability more generally. This study aimed to learn about the use of immunoglobulins in SID, identify themes concerning its use and access and suggest methods for improving access. This study included a thematic analysis of a published data set of 43 articles concerning immunoglobulin use and access in SID. The data set used to perform the thematic analysis is based on research articles identified from Excerpta Medica Database (EMBASE) and PubMed databases, published as part of a systematic review and part 1 of this two-part publication series. A thematic synthesis was conducted to identify recurrent themes. The three primary themes included (1) the context for IgRT prescription, which included patient characteristics and cost burden of IgRT administration, and its use in different countries; (2) factors contributing to inappropriate IgRT use, including health care professionals' awareness of IgRT, disparity between guidelines and actual clinical practice, and the effect of shortages on prescription and chemotherapy-induced hypogammaglobulinemia (HGG); and (3) measures identified to improve IgRT use and access, which included multidisciplinary involvement, improved diagnostic tools and safer withdrawal and stewardship protocols. IgRT use is increasing in HM as a supportive therapy but without comprehensive clinical guidelines and appropriate prescribing recommendations, medication wastage may occur with consequences for immunoglobulin access.

氮磷添加下典型温带、亚热带森林土壤氮循环功能基因丰度和微生物群落属性数据集

Nitrogen (N) and phosphorus (P) are essential nutrients for soil microbial growth and activities. There are notable differences in the concentrations of N and P and the characteristics of microbial communities observed between temperate and subtropical forests soils, and this is also true for soil vertical profiles. As a consequence of the rise in global atmospheric N deposition, soil nutrient imbalances and P limitation are increasingly aggravated in eastern China. However, there is a lack of systematic dataset showing how N deposition and P addition affect soil N cycling microbes and soil physicochemical properties in eastern China; hence a comprehensive dataset is needed to a better understanding of the impact of N deposition and P addition on soil nitrogen cycle. This dataset is constructed based on the field experimental setup in the forests of Chinese Ecosystem Research Network (CERN). To obtain a systematic dataset on N-cycling microbial communities, we conducted measurements and collected data of soil N-cycling functional genes abundance, N-cycling microbial community diversities and compositions and enzyme activities in three typical forests with N and/or P additions. We collected 0-10 cm top soils from a temperate forest (Changbai Mountain) and two subtropical forests (Qianyanzhou and Dinghu Mountain) with field N and/or P additions, and the soils along 0-80 cm vertical profiles without nutrient additions. The N-cycling functional genes involved in the datasets cover ammonia oxidizing archaea amoA, ammonia oxidizing bacteria amoA, nirK, nirS, nosZ, qnorB, narG, nir, nifH and chiA. The potential activities included in the datasets cover nitrification activity, denitrification activity, nitrogen fixation activity and organic nitrogen decomposition enzyme activity. The datasets also include soil physiochemical properties, including soil pH, moisture content, N2O emission, net nitrification rate and the concentrations of organic carbon, NH4+ , NO3− , available phosphorus, total nitrogen, total carbon, total phosphorus and dissolved organic carbon. The creation and sharing of the datasets can provide data and support for understanding the microbial mechanisms of soil N-cycling and the modelling processes under the scenario of aggravated N deposition and P addition. The datasets will also provide support for forest management efforts with regard to greenhouse gas N2O emission, N and nutrient loss in forest ecosystems.

Governments as borrowers and regulators

AbstractThe ability to borrow is important for government survival. Governments routinely resort to policies that privilege their own debt on financial markets, exploiting their dual role as borrowers and regulators. We label such policies as borrowing privileges. These borrowing privileges nudge investors to hold the government’s own debt. They share similarities with prudential regulation, but skew the market in favor of the government’s debt; and they share similarities with financial repression, but are less severe and thus consistent with the growth of financial markets. Introducing the first systematic dataset documenting the use of such policies across countries and over time, we demonstrate that governments implement borrowing privileges when their interactions with the global economy heighten fiscal needs: when borrowing costs indicate tightened access to credit, when trade liberalization undercuts revenue, and where fixed exchange rates increase the value of fiscal space. Despite the mobility of financial assets and constraints from global markets, governments retain latitude in regulating domestic markets to their own fiscal benefit.

The water-poor and reduced mantle beneath the Neo-Tethys Ocean: Tectonic setting of the western Yarlung-Tsangpo ophiolites

Water contents and deformation behaviors of minerals in mantle peridotites are diagnostic features to distinguish the physical conditions between mid-ocean ridges and subduction zones, which could serve as effective indicators for discriminating the tectonic setting of ophiolites. In this study, we report a systematic dataset of petrology, geochemistry, water contents and deformation behaviors for the mantle peridotites from the Cuobuzha ophiolite in the western Yarlung-Tsangpo suture zone (YTSZ), southern Tibet, to provide new insights into the long-standing debate for the tectonic setting of ophiolites therein. Geochemical data revealed that the Cuobuzha mantle peridotites experienced low to moderate degrees of partial melting (∼10–15%), although some harzburgites have been intensively modified by late-stage melt-rock interactions. The Cuobuzha peridotites, on the other hand, are characterized by low orthopyroxene water contents (46–95 wt ppm in average), clear olivine crystallographic preferred orientations of A- and E-types, and low oxygen fugacity (logfO2, QFM −2.55 to −0.81, where QFM means quartz-magnetite-fayalite buffer), thus indicative of the occurrence of high-temperature, water-depleted, and reduced mantle in this ophiolite. The features above of the Cuobuzha mantle peridotites are quite similar to those of abyssal peridotites at modern mid-ocean ridges and systematically different from the relatively refractory, water-rich, and oxidized characteristics typical of forearc peridotites in suprasubduction zones. Based on the data from this study and a compiled dataset for other ophiolites from the western YTSZ, we revealed that mantle peridotites with a water-poor and reduced nature are common in these ophiolites, thus providing strong evidence supporting for their origination from a mid-ocean ridge of a mature ocean basin. From the perspective of mantle peridotites, the ophiolites in the western YTSZ now geographically distributed in two branches (i.e., the northern and southern sub-belts) might have formed in a single ocean basin of the Neo-Tethys and were dismembered by late-stage tectonic events to their current locations.

Discovering functionally important sites in proteins

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

Surveys Under the Lens: How Public Administration Research Can Benefit from Citizen Survey Data

Abstract Analyses of comparative survey data can contribute to a more thorough understanding of citizens’ perceptions of and attitudes toward government. Thus, especially in public administration research, survey data can be a valuable research resource. This research note aims at providing an inventory of comprehensive, open-access survey data to advance quantitative public administration research about citizens’ attitudes. Applying a systematic dataset research, we found a total of 23 surveys useful for advancing public administration research, especially research on citizen–state interaction. As major contributions, first, the note sheds light on current survey status quo by capturing both data origin and usage in research. Second, it outlines an agenda for future research to draw scholarly attention to research opportunities in public administration research that could be addressed by the data.

CONSTRUCTING A PRODUCT ARCHITECTURE STRATEGY AND EFFECTS (PASE) MATRIX FOR EVALUATION AND SELECTION OF PRODUCT ARCHITECTURES

AbstractProduct architecture decisions are made early in the product development process and have far-reaching effects. Unless anticipated through experience or intuition, many of these effects may not be apparent until much later in the development process, making changes to the architecture costly in time, effort, and resources. Many researchers through the years have studied various elements of product architecture and their effects. By aggregating observations on the effects of architecture strategies from a selection of the literature on the topic and storing them in a systematic data set, this information can be recalled in a matrix structure which allows for the identification, comparison and evaluation, and then selection of the most desirable product architecture strategies before expending resources along any development path. This paper introduces this matrix, referred to as the Product Architecture Strategy and Effect (PASE) Matrix, how to construct one, and a demonstration of its use.

Visualization of the individual blood microbiome to study the etiology of sarcoidosis

IntroductionSingle microbial pathogens or host-microbiome dysbiosis are the causes of lung diseases with suspected infectious etiology. Metagenome sequencing provides an overview of the microbiome content. Due to the rarity of most granulomatous lung diseases collecting large systematic datasets is challenging. Thus, single-patient data often can only be summarized visually. ObjectiveTo increase the information gain from a single-case metagenome analysis we suggest a quantitative and qualitative approach. ResultsThe 16S metagenomic results of 7 patients with pulmonary sarcoidosis were compared with those of 22 healthy individuals. From lysed blood, total microbial DNA was extracted and sequenced. Cleaned data reads were identified taxonomically using Kraken 2 software. Individual metagenomic data were visualized with a Sankey diagram, Krona chart, and a heat-map. We identified five genera that were exclusively present or significantly enhanced in patients with sarcoidosis - Veillonella, Prevotella, Cutibacterium, Corynebacterium, and Streptococcus. ConclusionsOur approach can characterize the blood microbiome composition and diversity in rare diseases at an individual level. Investigation of the blood microbiome in patients with granulomatous lung diseases of unknown etiology, such as sarcoidosis could enhance our comprehension of their origin and pathogenesis and potentially uncover novel personalized therapeutics.

NBA Game Analysis based on Big Data

As an important sport, basketball has a history of more than 100 years. With the rapid development of basketball, data analysis technology has also been widely used. In basketball games, the data that people see is a set of systematic data formed by professional workers through division of labor and close cooperation, classification and statistical analysis. In addition, based on information on the field, spectators get a general idea of how athletes are performing during the game and predict where the data will go. Based on the above background, this paper discusses the significance and application of NBA game data analysis methods, and shows that in NBA games, data analysis can effectively promote the development of basketball.

Prediction of molecular distribution and temperature profile of FCC process through molecular-level kinetic modeling

This work aims to build a molecular-level process model framework to simulate the fluid catalytic cracking (FCC) process. The process model consists of the riser, regenerator, and separation models. A complex molecular-level kinetic model, including 3,652 molecules and 8,202 reactions, was developed for the heavy oil FCC process. The kinetic model was combined with the riser model, and a set of systematic pilot-scale FCC data was used to tune the kinetic parameter. Afterwards, a two-zone and two-phase regenerator model was built. The regenerator was combined with the riser model, and the coupled modeling and process simulation for the riser-type FCC unit was developed. The results indicate that the calculated values of product yields and key bulk properties agree well with the experimental data. Moreover, the molecular distribution of the product and temperature profile were predicted.

Subsurface flow paths in a chronosequence of calcareous soils: impact of soil age and rainfall intensities on preferential flow occurrence

Abstract. Soil hydrologic processes play an important role in the hydro-pedo-geomorphological feedback cycle of landscape evolution. Soil properties and subsurface flow paths both change over time, but due to a lack of observations, subsurface water flow paths are often not properly represented in soil and landscape evolution models. We investigated the evolution of subsurface flow paths across a soil chronosequence in the calcareous glacier forefield at the Griessfirn glacier in the Swiss Alps. Young soils developed from calcareous parent material usually have a high pH value, which likely affects vegetation development and pedogenesis and thus the evolution of subsurface flow paths. We chose four glacial moraines of different ages (110, 160, 4 900, and 13 500 years) and conducted sprinkling experiments with the dye tracer Brilliant Blue on three plots at each moraine. Each plot was divided into three equal subplots, and dyed water was applied with three different irrigation intensities (20, 40, and 60 mm h−1) and an irrigation amount of 40 mm. Subsequent excavation of soil profiles enabled the tracing of subsurface flow paths. A change in flow types with increasing moraine age was observed from a rather homogeneous matrix flow at 110 and 160 years to heterogeneous matrix and finger-shaped flow at 4 900 and 13 500 years. However, the proportion of preferential flow paths is not necessarily directly related to the moraine age but rather to soil properties such as texture, soil layering, organic matter content, and vegetation characteristics such as root length density and biomass. Irrigation intensity had an effect on the number of finger-shaped flow paths at the two old moraines. We also found that flow paths in this calcareous material evolved differently compared to a previous study in siliceous material, which emphasizes the importance of parent material for flow path evolution. Our study provides a rare systematic dataset and observations on the evolution of vertical subsurface flow paths in calcareous soils, which is useful to improve their representation in the context of landscape evolution modeling.

Enhanced commercial cooking inventories from the city scale through normalized emission factor dataset and big data

Cooking emission inventories always have poor spatial resolutions when applying with traditional methods, making their impacts on ambient air and human health remain obscure. In this study, we created a systematic dataset of cooking emission factors (CEFs) and applied it with a new data source, cooking-related point of interest (POI) data, to build up highly spatial resolved cooking emission inventories from the city scale. Averaged CEFs of six particulate and gaseous species (PM, OC, EC, NMHC, OVOCs, VOCs) were 5.92 ± 6.28, 4.10 ± 5.50, 0.05 ± 0.05, 22.54 ± 20.48, 1.56 ± 1.44, and 7.94 ± 6.27 g/h normalized in every cook stove, respectively. A three-field CEF index containing activity and emission factor species was created to identify and further build a connection with cooking-related POI data. A total of 95,034 cooking point sources were extracted from Beijing, as a study city. In downtown areas, four POI types were overlapped in the central part of the city and radiated into eight distinct directions from south to north. Estimated PM/VOC emissions caused by cooking activities in Beijing were 4.81/9.85 t per day. A 3D emission map showed an extremely unbalanced emission density in the Beijing region. Emission hotspots were seen in Central Business District (CBD), Sanlitun, and Wangjing in Chaoyang District and Willow and Zhongguancun in Haidian District. PM/VOC emissions could be as high as 16.6/42.0 kg/d in the searching radius of 2 km. For PM, the total emissions were 417.4, 389.0, 466.9, and 443.0 t between Q1 and Q4 2019 in Beijing, respectively. The proposed methodology is transferrable to other Chinese cities for deriving enhanced commercial cooking inventories and potentially highlighting the further importance of cooking emissions on air quality and human health.