Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 | Paper Citation Record

Observation 353b6126-93a6-48dd-b7aa-1912c04a7f6a · outbound

This paper cites Time-series large language models: A systematic review of state-of-the-art.IEEE Access, 13:30235–30261, 2025.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Time-series large language models: A systematic review of state-of-the-art.IEEE Access, 13:30235–30261, 2025

Reference 1

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Observation c6b3e653-43bc-4b93-84e1-82ce9a554048 · outbound

This paper cites in a.i. we trust?.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 in a.i. we trust?

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-07-10T14:57:14.411510Z

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Observation 79da29e0-8f1d-4a88-bd2d-818bc25fb000 · outbound

This paper cites Using VIIRS fire radiative power data to simulate biomass burning emissions, plume rise and smoke transport in a real-time air quality modeling system.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Using VIIRS fire radiative power data to simulate biomass burning emissions, plume rise and smoke transport in a real-time air quality modeling system

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.615577Z

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Observation 0ffaf9dd-9e86-4023-8bba-6748c7ac7c45 · outbound

This paper cites Chronos: Learning the Language of Time Series.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Chronos: Learning the Language of Time Series

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-10T14:57:14.480720Z

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Observation 848abcd4-dd6a-4889-86ea-5d10655672dd · outbound

This paper cites Chronos-2: From Univariate to Universal Forecasting.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Chronos-2: From Univariate to Universal Forecasting

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-10T14:57:14.478222Z

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Observation 324ccff2-c163-4fd2-bc48-338cdd6d8698 · outbound

This paper cites Bhowmik, Youn Soo Jung, Juan A.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Bhowmik, Youn Soo Jung, Juan A

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.631055Z

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Observation e5acaa84-11b2-4f85-be9c-8439a634c49d · outbound

This paper cites Cross-regional deep learning for air quality forecasting: A comparative study of co, no2, o3, pm2.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Cross-regional deep learning for air quality forecasting: A comparative study of co, no2, o3, pm2

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.604846Z

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Observation 4cbe47f7-77f0-49ad-9464-1d19e914c998 · outbound

This paper cites The changing risk and burden of wildfire in the United States.Proceedings of the National Academy of Sciences, 118(2), 2021.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 The changing risk and burden of wildfire in the United States.Proceedings of the National Academy of Sciences, 118(2), 2021

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.608340Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bd22b73c-6e0e-43d7-9407-de03cd160fb8 · outbound

This paper cites an unresolved cited work.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-07-10T14:57:14.606387Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e344290-6bd8-4d46-85d1-1ca6a90c1a7f · outbound

This paper cites This time is different: An observability perspective on time series foundation models.Advances in neural information processing systems, 38:50907–50951, 2026.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 This time is different: An observability perspective on time series foundation models.Advances in neural information processing systems, 38:50907–50951, 2026

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.619109Z

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Observation e4b863df-69d9-42f5-a325-0330889b005e · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 A decoder-only foundation model for time-series forecasting

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.637749Z

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Observation 42a18300-b95e-4d35-ae9b-d1305e229a61 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.639601Z

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Observation 28dd5ea4-a4b4-4d7d-ad89-80e196522ab9 · outbound

This paper cites Freeman, Graham Taylor, Bahram Gharabaghi, and Jesse Th´ e.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Freeman, Graham Taylor, Bahram Gharabaghi, and Jesse Th´ e

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.611941Z

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Observation 55d35951-f29e-4a60-bfc2-8cd8d5de8e28 · outbound

This paper cites MOMENT: A family of open time-series foundation models.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 MOMENT: A family of open time-series foundation models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.632809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bfc05264-27c3-4182-8313-f81f9a73a85f · outbound

This paper cites Grell, Steven E.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Grell, Steven E

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.653384Z

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Observation 8ae5be6f-1167-4890-8736-338b83e7205a · outbound

This paper cites Long short-term memory.Neural Computation, 9(8):1735–1780.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Long short-term memory.Neural Computation, 9(8):1735–1780

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.613741Z

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Observation 00de60ac-33fb-4a64-acf6-d7d9d0d83485 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.625142Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9ce3a801-1b65-4309-829f-f5ad6d127f74 · outbound

This paper cites Jaffe, Susan M.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Jaffe, Susan M

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.636001Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bd8f8be7-7049-4957-a14a-12f7b3a5010a · outbound

This paper cites an unresolved cited work.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unresolved cited work

Reference 19

Resolution: verified exact; doi, observed 2026-07-10T14:57:14.424757Z

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Observation e31843ba-d044-479b-ad85-a43fb5ad5988 · outbound

This paper cites Kumar and B.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Kumar and B

Reference 20

Resolution: verified exact; doi, observed 2026-07-10T14:57:14.401621Z

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Observation c7e3bc3b-236d-4c05-8d98-2a190a3ec898 · outbound

This paper cites doi: 10.1126/science.adi2336.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 doi: 10.1126/science.adi2336

Reference 21

Resolution: metadata mismatch; doi, observed 2026-07-10T14:57:14.426996Z

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Observation 7614247d-5cfc-4687-afac-e4e9307194d7 · outbound

This paper cites Multi-scale spectral recurrent network based on random fourier features for wind speed forecasting.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Multi-scale spectral recurrent network based on random fourier features for wind speed forecasting

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.651612Z

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Observation 786b6735-0511-4a15-b149-13a8f929b1d8 · outbound

This paper cites Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.Environmental Pollution, 231:997–1004, 2017.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.Environmental Pollution, 231:997–1004, 2017

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.644974Z

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Observation 35811dd2-89f9-404e-b03a-34a1a3902acc · outbound

This paper cites an unresolved cited work.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-07-10T14:57:14.646507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b3d0b598-16d1-4d90-92b2-f9894b1f1cd8 · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Foundation models for time series analysis: A tutorial and survey

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Resolution: metadata mismatch; arxiv_id, observed 2026-07-10T14:57:14.407120Z

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Observation 662969c8-a336-4ad3-b265-978e0ff16c67 · outbound

This paper cites Lamb, and Pierre Gentine.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Lamb, and Pierre Gentine

Reference 26

Resolution: verified exact; doi, observed 2026-07-10T14:57:14.418148Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a8e75a0f-fccf-472a-bf9b-5c1060142566 · outbound

This paper cites Moirai 2.0: When less is more for time series forecasting.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Moirai 2.0: When less is more for time series forecasting

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-10T14:57:14.475661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 219f5711-d5c0-4451-9039-d20e2a3fdbb8 · outbound

This paper cites Timer: Generative pre-trained transformers are large time series models.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Timer: Generative pre-trained transformers are large time series models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.648223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5d8cb2c1-1327-4cc9-8748-43c3a530a980 · outbound

This paper cites Sundial: A family of highly capable time series foundation models.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Sundial: A family of highly capable time series foundation models

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.655344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a212a7df-67b7-4027-aba4-a652e59c2416 · outbound

This paper cites an unresolved cited work.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unresolved cited work

Reference 30

Resolution: verified exact; doi, observed 2026-07-10T14:57:14.435956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a65190bf-c52a-435d-a9ce-f08cfd2899d0 · outbound

This paper cites From pre-training to post-training: A survey on time series foundation models.TechRxiv,.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 From pre-training to post-training: A survey on time series foundation models.TechRxiv,

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.623386Z

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Observation 72effddd-e2b3-4c7a-98e5-099d0ddadf11 · outbound

This paper cites an unresolved cited work.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unresolved cited work

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-10T14:57:14.420922Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9008329c-1608-4a8b-8543-b74b2e8041e5 · outbound

This paper cites Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola

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Resolution: verified exact; doi, observed 2026-07-10T14:57:14.434306Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 214ac9d1-1a60-4c51-932a-4c5685e486f6 · outbound

This paper cites Lag-Llama: Towards foundation models for probabilistic time series forecasting, 2023.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Lag-Llama: Towards foundation models for probabilistic time series forecasting, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.629263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 794ddad7-d355-4fc1-bbd9-55a2102f6f89 · outbound

This paper cites Reid, Michael Brauer, Fay H.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Reid, Michael Brauer, Fay H

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.617322Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 28c9b518-e53a-41a6-825d-636fa79e1590 · outbound

This paper cites an unresolved cited work.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unresolved cited work

Reference 36

Resolution: verified exact; doi, observed 2026-07-10T14:57:14.437779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9618402c-3076-47c4-8048-5251e3345ba2 · outbound

This paper cites an unresolved cited work.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unresolved cited work

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-10T14:57:14.404185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b5e0e41e-7e57-4f6c-99fa-2a3461a5f431 · outbound

This paper cites Time-MoE: Billion-scale time series foundation models with mixture of experts.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Time-MoE: Billion-scale time series foundation models with mixture of experts

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.641359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 04a6aca1-6fd4-4ddf-9f68-f29dd18aec82 · outbound

This paper cites NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System

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Resolution: verified exact; doi, observed 2026-07-10T14:57:14.422804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8c7cf293-6ac6-4956-8a7d-10c0eba5a232 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Gomez, Lukasz Kaiser, and Illia Polosukhin

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.649873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 48577a6b-6b01-4e81-84be-979b91c1e130 · outbound

This paper cites ChatTime: A unified multimodal time series foundation model bridging numerical and textual data.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 ChatTime: A unified multimodal time series foundation model bridging numerical and textual data

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.610084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bd25eef7-8ae0-4b63-b8ee-701b7d2c142d · outbound

This paper cites Unified training of universal time series forecasting transformers.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unified training of universal time series forecasting transformers

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.620919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 50103a51-8c18-4e38-8ffb-876ee0a67f78 · outbound

This paper cites World Health Organization, Geneva, 2021.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 World Health Organization, Geneva, 2021

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.634491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d0d24463-dc27-4556-b565-b4cb2c6eb049 · outbound

This paper cites Spatial deep learning for PM2.5 estimation during extreme pollution events: Quantifying uncertainty and training with sparse datasets.Environmental Research: Atmospheres, 2026.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Spatial deep learning for PM2.5 estimation during extreme pollution events: Quantifying uncertainty and training with sparse datasets.Environmental Research: Atmospheres, 2026

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Resolution: verified exact; doi, observed 2026-07-10T14:57:14.429225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d843e057-4b2e-40c7-b923-398423d2bb03 · outbound

This paper cites an unresolved cited work.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Unresolved cited work

Reference 45

Resolution: verified exact; doi, observed 2026-07-10T14:57:14.408790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b4175db7-eff6-48fb-9766-cd79fafb75e3 · outbound

This paper cites Predicting hourly PM2.5 concentrations in wildfire-prone areas using a spatiotemporal transformer model.Science of The Total Environment, 860:160446, 2023.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Predicting hourly PM2.5 concentrations in wildfire-prone areas using a spatiotemporal transformer model.Science of The Total Environment, 860:160446, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.627497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation afb1b550-068d-485b-863b-36027274b842 · outbound

This paper cites Evaluating deep learning time series models for PM2.5 forecasting across diverse horizons.iScience, 29(2): 114770, 2026.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Evaluating deep learning time series models for PM2.5 forecasting across diverse horizons.iScience, 29(2): 114770, 2026

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-10T14:57:14.432193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e5e0f8d4-32f1-4505-b413-8860655c10cd · outbound

This paper cites A spatiotemporal multimodal framework for air pollution prediction based on bayesian optimization: Evidence from Sichuan, China.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 A spatiotemporal multimodal framework for air pollution prediction based on bayesian optimization: Evidence from Sichuan, China

Reference 48

Resolution: verified exact; doi, observed 2026-07-10T14:57:14.413613Z

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Observation 9ae0bfd2-ddd2-4b1d-b9f3-9fb689c211b3 · outbound

This paper cites Real-time air quality forecasting, part I: History, techniques, and current status.Atmospheric Environment, 60:632–655, 2012.

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5 Real-time air quality forecasting, part I: History, techniques, and current status.Atmospheric Environment, 60:632–655, 2012

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T14:57:14.643155Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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