cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.hpaexb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gymnotiformes",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0257541-220831081235567/datasets/export for details.
DOI: 10.15468/dl.gt245d
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eutrema wuchengyii (Al-Shehbaz, T.Y.Cheo, L.L.Lu & G.Yang) Al-Shehbaz & S.I.Warwick",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0264165-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pxd3t9
title: Occurrence Download

description: A dataset containing 598768 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vulpes vulpes (Linnaeus, 1758)"
 ]
}

 The dataset includes 598768 records from 2155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0257360-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ecnwy8
title: Occurrence Download

description: A dataset containing 625 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-77.88786 -55.41517,-67.58735 -55.41517,-67.58735 -17.20038,-77.88786 -17.20038,-77.88786 -55.41517))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acacia dealbata Link",
 "Year 2010-2025"
 ]
}

 The dataset includes 625 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015138-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kzgc5b
title: Occurrence Download

description: A dataset containing 1941 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Polistes instabilis de Saussure, 1853"
 ]
}

 The dataset includes 1941 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015143-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nye22b
title: Occurrence Download

description: A dataset containing 6109 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((117.80577 3.8988,115.8436 4.39765,115.18955 2.63125,114.53549 1.40087,113.26585 1.20855,112.34248 1.47779,111.41911 1.05469,110.72658 0.7854,109.88016 1.32394,109.57237 1.90082,108.87984 0.7854,109.88016 -3.25258,116.5746 -4.36583,119.49862 0.82387,117.80577 3.8988))",
 "HasGeospatialIssue is false",
 "TaxonKey is Polypodiopsida"
 ]
}

 The dataset includes 6109 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015139-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14207362
title: GlobalHighCO: Global Daily Seamless 1 km Ground-Level CO Dataset over Land (2018–Present)

description: GlobalHighCO is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level CO dataset over land from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.93 and a root-mean-square error (RMSE) of 0.21 mg m-3 on a daily basis.

More GHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in UTC time (i.e., GMT+0), and all data will be made publicly available online once our paper is accepted.

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.14207363
title: GlobalHighCO: Global Daily Seamless 1 km Ground-Level CO Dataset over Land (2018–Present)

description: GlobalHighCO is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level CO dataset over land from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.93 and a root-mean-square error (RMSE) of 0.21 mg m-3 on a daily basis.

More GHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in UTC time (i.e., GMT+0), and all data will be made publicly available online once our paper is accepted.

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.15468/dl.akbfxc
title: Occurrence Download

description: A dataset containing 21953446 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 21953446 records from 488 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015141-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29133731
title: data.zip

description: LLM training
DOI: 10.5281/zenodo.10403497
title: GlobalHighPM₂.₅ (2017)

description: Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM2.5 dataset over land for the year 2017. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.91 and a root-mean-square error (RMSE) of 9.20 µg m-3 on a daily basis.

If you use the GlobalHighPM2.5 dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):





Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3
description: Note that the data are recorded in UTC time (i.e., GMT+0). 

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.10801181
title: GlobalHighPM₂.₅ (2017)

description: Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM2.5 dataset over land for the year 2017. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.91 and a root-mean-square error (RMSE) of 9.20 µg m-3 on a daily basis.

If you use the GlobalHighPM2.5 dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):





Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3
description: Note that the data are recorded in UTC time (i.e., GMT+0). 

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.25550/83-y2nm
title: Sagittal section of Pthrp mCherry/+ (Control) mandible at postnatal day 3 (P3).

description: At P3, PTHrP-mCherry positive cells were preferentially localized in a manner surrounding SOX9 positive cells in the mandible.
DOI: 10.5281/zenodo.15489963
title: Rapid Reversal of Hemispheric Asymmetry in the Intensity of EIA Crests During the Geomagnetic Storm of April 23, 2023: A Unique Observation from GOLD

description: AMIE TIEGCM simulation of neutral wind and electron density on 22 and 23 April 2023.
DOI: 10.5281/zenodo.15489964
title: Rapid Reversal of Hemispheric Asymmetry in the Intensity of EIA Crests During the Geomagnetic Storm of April 23, 2023: A Unique Observation from GOLD

description: AMIE TIEGCM simulation of neutral wind and electron density on 22 and 23 April 2023.
DOI: 10.5281/zenodo.15348419
title: 2022-2023 routine monitoring data of Panyang River in Guangxi, China
DOI: 10.5281/zenodo.15348418
title: 2022-2023 routine monitoring data of Panyang River in Guangxi, China

description: 广西喀斯特复兴河-盘阳河上游向下游月度数据集(2022-2023)
DOI: 10.5281/zenodo.15378971
title: 2022-2023 routine monitoring data of Panyang River in Guangxi, China

description: 广西喀斯特复兴河-盘阳河上游向下游月度数据集(2022-2023)
DOI: 10.15468/dl.jgmhjg
title: Occurrence Download

description: A dataset containing 84508 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mimus polyglottos (Linnaeus, 1758)"
 ]
}

 The dataset includes 84508 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015142-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14457/tu.the.2024.258
title: การศึกษาการใช้วัสดุรูพรุนในการลดก๊าซแอมโมเนียที่เกิดขึ้นในการผลิตวัสดุจีโอพอลิเมอร์จากกากตะกรันอะลูมิเนียม

description: ในงานวิจัยนี้ได้ทำการผลิตวัสดุจีโอพอลิเมอร์จากกากตะกรันอะลูมิเนียม (Aluminium dross residue, AD) ในปริมาณ 0 ถึง 100 เปอร์เซ็นโดยน้ำหนัก ซึ่งเป็นของเสียจากโรงงานรีไซเคิลอะลูมิเนียม ในส่วนนี้มักพบปัญหาการลักลอบทิ้งและมีค่าใช้จ่ายในการกำจัดสูง ในงานวิจัยนี้จึงได้นำกากตะกรันอะลูมิเนียมมาเป็นวัตถุดิบหลัก และเถ้าลอยของเสียจากการผลิตโรงงานผลิตไฟฟ้าแม่เมาะ จังหวัดลำปาง โดยในกระบวนการผลิตได้ใช้สารละลายอัลคาไลน์เพื่อกระตุ้นการเกิดปฏิกิริยาจีโอพอ-ลิเมอไรเซชัน ซึ่งเป็นสารละลายผสมระหว่างโซเดียมไฮดรอกไซด์ (Sodium hydroxide, NaOH) ความเข้มข้น 7 โมลาร์ และโซเดียมซิลิเกต (Sodium silicate,Na2SiO3) ในอัตราส่วน 1:3 เมื่อกากตะกรันอะลูมิเนียมสัมผัสกับความชื้นหรือน้ำ ทำให้เกิดกลิ่นเหม็นฉุนรุนแรงของก๊าซแอมโมเนีย ในงานวิจัยนี้เลือกวัสดุรูพรุน 6 ชนิด ได้แก่ เพอร์ไลท์ (Pearlite:PE), เถ้าชานอ้อย (Bagasse ash:BA), ดินเบา (Diatomite:DI), สเมกไทด์ (Smectite:SM) และซีโอไลท์ (Zeolite:ZE) เพื่อลดการเกิดก๊าซแอมโมเนียและดับกลิ่นไม่พึงประสงค์ที่เป็นอันตรายต่อร่างกายในระหว่างการเกิดปฏิกิริยา โดยใช้อัตราส่วนระหว่างของสารละลายอัลคาไลน์ (ของเหลว) กับส่วนของกากตะกรันอะลูมิเนียมและวัสดุรูพรุน (ของแข็ง) เท่ากับ 0.5 พบว่าการเติม AD ลงไปในชิ้นงานทำให้เกิดก๊าซแอมโมเนีย ซึ่งส่งผลให้เกิดเป็นรูพรุนจำนวนมากในชิ้นงาน ทำให้ชิ้นงานจีโอพอลิเมอร์มีกำลังรับแรงอัดต่ำ เปอร์เซ็นต์การดูดซึมน้ำสูง ความหนาแน่นต่ำ และการนำความร้อนต่ำ ผลการทดสอบชิ้นงานจีโอพอลิเมอร์จากกากตะกรันอะลูมิเนียมที่มีการเติมเถ้าลอย เถ้าชานอ้อย เพอร์ไลท์ และซีโอไลท์ ให้ผลการทดสอบที่ดีสามารถดูดซับก๊าซแอมโมเนียได้ดี ผลการทดสอบสมบัติเชิงกลค่าความหนาแน่นสูง ค่ากำลังรับแรงอัดสูง และค่าเปอร์เซ็นต์การดูดซึมน้ำต่ำ ส่วนการดินเบา (DI) และสเมกไทด์ (SM) ลงในชิ้นงานจีโอโพลิเมอร์จากกากตะกรันอะลูมิเนียม (AD) ในปริมาณมากขึ้น ทำให้ชิ้นงานเซ็ตตัวได้ช้า เกิดรูพรุนและช่องว่างในโครงสร้างจำนวนมาก ทำให้ค่าความหนาแน่นต่ำ เปอร์เซ็นการดูดซึมน้ำสูง ส่งผลให้ชิ้นงานสามารถทนต่อแรงอัดได้น้อย นอกจากนั้นได้ทำการศึกษาโครงสร้างเฟส โครงสร้างพันธะเคมี องค์ประกอบทางเคมี และโครงสร้างจุลภาคของชิ้นงานจีโอพอลิเมอร์จากกากตะกรันอะลูมิเนียมด้วยเทคนิค XRD, FTIR, XRF และ SEM ตามลำดับ
DOI: 10.15468/dl.smtt3w
title: Occurrence Download

description: A dataset containing 78460 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "Geometry POLYGON((-76.38273 6.12504,-75.8992 6.12176,-75.89091 6.55313,-76.40278 6.53726,-76.38273 6.12504))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 78460 records from 180 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015144-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10081359
title: GlobalHighPM2.5: Big Data Gapless 1 km Global Ground-level PM2.5 Dataset over Land

description: GlobalHighPM2.5 is one of the series of long-term, full-coverage, global high-resolution and high-quality datasets of ground-level air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. 

This dataset contains input data, analysis codes, and generated dataset used for the following article, and if you use the GlobalHighPM2.5 dataset for related scientific research, please cite the below-listed corresponding reference (Wei et al., NC, 2023):





Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3



Input Data

Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.

Code

Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.

Generated Dataset

Here is the first big data-derived gapless (spatial coverage = 100%) monthly and yearly 1 km (i.e., M1K, and Y1K) global ground-level PM2.5 dataset over land from 2017 to 2022. This dataset yields a high quality with cross-validation coefficient of determination (CV-R2) values of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m-3 on the daily, monthly, and annual basises, respectively.

Due to data volume limitations, 

        all (including daily) data for the year 2022 is accessible at: GlobalHighPM2.5 (2022)

        all (including daily) data for the year 2021 is accessible at: GlobalHighPM2.5 (2021)

        all (including daily) data for the year 2020 is accessible at: GlobalHighPM2.5 (2020)

        all (including daily) data for the year 2019 is accessible at: GlobalHighPM2.5 (2019)

        all (including daily) data for the year 2018 is accessible at: GlobalHighPM2.5 (2018)

        all (including daily) data for the year 2017 is accessible at: GlobalHighPM2.5 (2017)

        Continuously updated...

More air quality datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in UTC time (i.e., GMT+0). This dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing@umd.edu).
DOI: 10.5281/zenodo.10800980
title: GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)

description: GlobalHighPM2.5 is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

This dataset contains input data, analysis codes, and generated dataset used for the following article. If you use the GlobalHighPM2.5 dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):





Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3



Input Data

Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.

Code

Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.

Generated Dataset

Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM2.5 dataset over land from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R2) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m-3 on the daily, monthly, and annual bases, respectively.

Due to data volume limitations, 

        all (including daily) data for the year 2022 is accessible at: GlobalHighPM2.5 (2022)

        all (including daily) data for the year 2021 is accessible at: GlobalHighPM2.5 (2021)

        all (including daily) data for the year 2020 is accessible at: GlobalHighPM2.5 (2020)

        all (including daily) data for the year 2019 is accessible at: GlobalHighPM2.5 (2019)

        all (including daily) data for the year 2018 is accessible at: GlobalHighPM2.5 (2018)

        all (including daily) data for the year 2017 is accessible at: GlobalHighPM2.5 (2017)

        continuously updated...

More GHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in UTC time (i.e., GMT+0).

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.6449740
title: GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)

description: GlobalHighPM2.5 is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

This dataset contains input data, analysis codes, and generated dataset used for the following article. If you use the GlobalHighPM2.5 dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):





Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3



Input Data

Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.

Code

Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.

Generated Dataset

Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM2.5 dataset over land from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R2) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m-3 on the daily, monthly, and annual bases, respectively.

Due to data volume limitations, 

        all (including daily) data for the year 2022 is accessible at: GlobalHighPM2.5 (2022)

        all (including daily) data for the year 2021 is accessible at: GlobalHighPM2.5 (2021)

        all (including daily) data for the year 2020 is accessible at: GlobalHighPM2.5 (2020)

        all (including daily) data for the year 2019 is accessible at: GlobalHighPM2.5 (2019)

        all (including daily) data for the year 2018 is accessible at: GlobalHighPM2.5 (2018)

        all (including daily) data for the year 2017 is accessible at: GlobalHighPM2.5 (2017)

        continuously updated...

More GHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in UTC time (i.e., GMT+0).

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.6449741
title: GlobalHighPM2.5: Big Data Seamless 1 km Global Ground-level PM2.5 Dataset over Land

description: GlobalHighPM2.5 is one of the series of long-term, full-coverage, global high-resolution and high-quality datasets of ground-level air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. 
Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM2.5 dataset over land from 2017 to 2022. This dataset yields a high quality with cross-validation coefficient of determination (CV-R2) values of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 μg/m3 on the daily, monthly, and annual basises, respectively.
Note that this dataset is closed since a new version is published at 10.5281/zenodo.10081359.
description: All the data will be made publicly available online once our paper is published. Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please feel free to contact us (Email: weijing@umd.edu or weijing_rs@163.com; zhanqing@umd.edu).