{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:00:48Z","timestamp":1773900048597,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001695","name":"JST PRESTO","doi-asserted-by":"publisher","award":["JPMJPR16O3"],"award-info":[{"award-number":["JPMJPR16O3"]}],"id":[{"id":"10.13039\/501100001695","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001695","name":"JST PRESTO","doi-asserted-by":"publisher","award":["16KK0169"],"award-info":[{"award-number":["16KK0169"]}],"id":[{"id":"10.13039\/501100001695","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001695","name":"JST PRESTO","doi-asserted-by":"publisher","award":["19K15944"],"award-info":[{"award-number":["19K15944"]}],"id":[{"id":"10.13039\/501100001695","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000646","name":"JSPS KA-KENHI","doi-asserted-by":"publisher","award":["JPMJPR16O3"],"award-info":[{"award-number":["JPMJPR16O3"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000646","name":"JSPS KA-KENHI","doi-asserted-by":"publisher","award":["16KK0169"],"award-info":[{"award-number":["16KK0169"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000646","name":"JSPS KA-KENHI","doi-asserted-by":"publisher","award":["19K15944"],"award-info":[{"award-number":["19K15944"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Reliable, high-resolution emission inventories are essential for accurately simulating air quality and for designing evidence-based mitigation policies. Yet their performance over Japan\u2014where transboundary inflow, strict fuel regulations, and complex source mixes coexist\u2014remains poorly quantified. This study therefore benchmarks four widely used anthropogenic inventories\u2014REAS v3.2.1, CAMS-GLOB-ANT v6.2, ECLIPSE v6b, and HTAP v3\u2014by coupling each to WRF-Chem (10 km grid) and comparing simulated surface PM2.5, SO2, CO, and NOx with observations from &gt;900 stations across eight Japanese regions for the years 2010 and 2015. All simulations shared identical meteorology, chemistry, and natural-source inputs (MEGAN 2.1 biogenic VOCs; FINN v1.5 biomass burning) so that differences in model output isolate the influence of anthropogenic emissions. HTAP delivered the most balanced SO2 and CO fields (regional mean biases mostly within \u00b125%), whereas ECLIPSE reproduced NOx spatial gradients best, albeit with a negative overall bias. REAS captured industrial SO2 reliably but over-estimated PM2.5 and NOx in western conurbations while under-estimating them in rural prefectures. CAMS-GLOB-ANT showed systematic biases\u2014under-estimating PM2.5 and CO yet markedly over-estimating SO2\u2014highlighting the need for Japan-specific sulfur-fuel adjustments. For several pollutant\u2013region combinations, absolute errors exceeded 100%, confirming that emissions uncertainty, not model physics, dominates regional air quality error even under identical dynamical and chemical settings. These findings underscore the importance of inventory-specific and pollutant-specific selection\u2014or better, multi-inventory ensemble approaches\u2014when assessing Japanese air quality and formulating policy. Routine assimilation of ground and satellite data, together with inverse modeling, is recommended to narrow residual biases and improve future inventories.<\/jats:p>","DOI":"10.3390\/data10090151","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T17:08:17Z","timestamp":1758560897000},"page":"151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Validation of Anthropogenic Emission Inventories in Japan: A WRF-Chem Comparison of PM2.5, SO2, NOx and CO Against Observations"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6763-6909","authenticated-orcid":false,"given":"Kenichi","family":"Tatsumi","sequence":"first","affiliation":[{"name":"Graduate School of Data Science, Nagoya City University, Nagoya 467-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen Thi Hong","family":"Diep","sequence":"additional","affiliation":[{"name":"College of Environment and Natural Resources, Can Tho University, Can Tho City 94115, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021). 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