Derivation of aerosol optical properties from satellite AOD data and low-cost Particulate Matter sensors Article Swipe
Giorgia Proietti Pelliccia
,
Tiziano Maestri
,
Erika Brattich
,
Federico Porcù
,
Silvana Di Sabatino
,
Francesco Barbano
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu24-19279
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu24-19279
The numerous issues caused to human health by aerosol pollution are well documented and certified since long time (e.g. Brunekreef and Holgate 2002). To date, the monitoring of aerosol levels in urban areas is conducted mainly through standardized procedures based on in-situ measurements at fixed and mobile stations. In the last two decades, the possibility of deriving PM values from satellite observations has been investigated (Hoff and Christopher 2009). A large variety of innovative methodologies relies on the retrieval of the Aerosol Optical Depth (AOD) from satellite measured reflectivity at shortwave wavelengths. AOD, once merged with auxiliary data related to meteorology, allows the estimation of the PM concentration over widespread urban and remote locations.Statistical and Machine Learning approaches have been often applied to investigate the correlation between PM concentration and AOD (Ma et al. 2022). Nevertheless, the physical interpretation is sometimes hidden by the complex nature of the relation and by the specificities of the studied areas. On the other hand, the derivation of rigorous physical laws requires a thorough investigation of the physico-chemical relationships between aerosol composition and optical properties.Most studies use measurements of mass concentration at dry conditions and apply corrections to account for the effect of humidity, which causes aerosol particles to grow. Only few research studies so far have directly considered measurements performed at ambient conditions, such as those operated by Optical Particle Counters (OPC) (e.g. Gupta et al. 2018). Particle number concentration measurements from OPCs and more so PM concentrations derived from such measurements are affected by humidity because of particle hygroscopic growth. Similarly, satellite algorithms used to retrieve AOD are affected by the aerosol hygroscopicity and the estimated AOD amounts is due to both the dry and humid component of the aerosol layer.This study applies a physical approach, investigates the possibility of using measurements from low-cost OPC sensors together with satellite AOD data to derive the relationship between aerosol concentration and their optical properties and, from that, to derive humidity-dependent properties. The study starts from the theoretical definition of AOD and PSD; it also uses information about local aerosol composition and PSD, and optical aerosol properties from model simulations.References:Brunekreef, Bert, and Stephen T. Holgate. 2002. ‘Air Pollution and Health’. The Lancet 360 (9341): 1233–42Gupta, P., P. Doraiswamy, R. Levy, O. Pikelnaya, J. Maibach, B. Feenstra, Andrea Polidori, F. Kiros, and K. C. Mills. 2018. ‘Impact of California Fires on Local and Regional Air Quality: The Role of a Low-Cost Sensor Network and Satellite Observations’. GeoHealth 2 (6): 172–81Hoff, Raymond M., and Sundar A. Christopher. 2009. ‘Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?’ Journal of the Air & Waste Management Association 59 (6): 645–75Ma, Zongwei, Sagnik Dey, Sundar Christopher, Riyang Liu, Jun Bi, Palak Balyan, and Yang Liu. 2022. ‘A Review of Statistical Methods Used for Developing Large-Scale and Long-Term PM2.5 Models from Satellite Data’. Remote Sensing of Environment 269 (February): 112827
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-egu24-19279
- OA Status
- gold
- Related Works
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- OpenAlex ID
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https://openalex.org/W4392652782Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-egu24-19279Digital Object Identifier
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Derivation of aerosol optical properties from satellite AOD data and low-cost Particulate Matter sensorsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-03-11Full publication date if available
- Authors
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Giorgia Proietti Pelliccia, Tiziano Maestri, Erika Brattich, Federico Porcù, Silvana Di Sabatino, Francesco BarbanoList of authors in order
- Landing page
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https://doi.org/10.5194/egusphere-egu24-19279Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.5194/egusphere-egu24-19279Direct OA link when available
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Particulates, Aerosol, Satellite, Environmental science, Remote sensing, Meteorology, Physics, Geology, Chemistry, Organic chemistry, AstronomyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Used | 465 |
| abstract_inverted_index.Yang | 457 |
| abstract_inverted_index.also | 339 |
| abstract_inverted_index.and, | 320 |
| abstract_inverted_index.been | 63, 119 |
| abstract_inverted_index.both | 279 |
| abstract_inverted_index.data | 97, 308 |
| abstract_inverted_index.from | 59, 85, 238, 246, 300, 321, 330, 352, 426, 473 |
| abstract_inverted_index.have | 118, 212 |
| abstract_inverted_index.last | 50 |
| abstract_inverted_index.laws | 166 |
| abstract_inverted_index.long | 16 |
| abstract_inverted_index.mass | 185 |
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| abstract_inverted_index.once | 93 |
| abstract_inverted_index.over | 108 |
| abstract_inverted_index.such | 220, 247 |
| abstract_inverted_index.time | 17 |
| abstract_inverted_index.used | 261 |
| abstract_inverted_index.uses | 340 |
| abstract_inverted_index.well | 11 |
| abstract_inverted_index.with | 95, 305 |
| abstract_inverted_index.(AOD) | 84 |
| abstract_inverted_index.(Hoff | 65 |
| abstract_inverted_index.(OPC) | 228 |
| abstract_inverted_index.(e.g. | 18, 229 |
| abstract_inverted_index.2002. | 360 |
| abstract_inverted_index.2009. | 420 |
| abstract_inverted_index.2018. | 389 |
| abstract_inverted_index.2022. | 459 |
| abstract_inverted_index.Bert, | 355 |
| abstract_inverted_index.Depth | 83 |
| abstract_inverted_index.Fires | 393 |
| abstract_inverted_index.Gupta | 230 |
| abstract_inverted_index.Levy, | 374 |
| abstract_inverted_index.Local | 395 |
| abstract_inverted_index.PM2.5 | 471 |
| abstract_inverted_index.Palak | 454 |
| abstract_inverted_index.Waste | 439 |
| abstract_inverted_index.about | 342 |
| abstract_inverted_index.apply | 191 |
| abstract_inverted_index.areas | 32 |
| abstract_inverted_index.based | 39 |
| abstract_inverted_index.date, | 24 |
| abstract_inverted_index.fixed | 44 |
| abstract_inverted_index.grow. | 205 |
| abstract_inverted_index.hand, | 160 |
| abstract_inverted_index.human | 5 |
| abstract_inverted_index.humid | 283 |
| abstract_inverted_index.large | 70 |
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| abstract_inverted_index.model | 353 |
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| abstract_inverted_index.other | 159 |
| abstract_inverted_index.since | 15 |
| abstract_inverted_index.study | 289, 328 |
| abstract_inverted_index.that, | 322 |
| abstract_inverted_index.their | 317 |
| abstract_inverted_index.those | 222 |
| abstract_inverted_index.urban | 31, 110 |
| abstract_inverted_index.using | 298 |
| abstract_inverted_index.which | 200 |
| abstract_inverted_index.112827 | 482 |
| abstract_inverted_index.2002). | 22 |
| abstract_inverted_index.2009). | 68 |
| abstract_inverted_index.2018). | 233 |
| abstract_inverted_index.2022). | 134 |
| abstract_inverted_index.Andrea | 381 |
| abstract_inverted_index.Kiros, | 384 |
| abstract_inverted_index.Lancet | 366 |
| abstract_inverted_index.Mills. | 388 |
| abstract_inverted_index.Models | 472 |
| abstract_inverted_index.Remote | 476 |
| abstract_inverted_index.Review | 461 |
| abstract_inverted_index.Riyang | 450 |
| abstract_inverted_index.Sagnik | 446 |
| abstract_inverted_index.Sensor | 405 |
| abstract_inverted_index.Space: | 427 |
| abstract_inverted_index.Sundar | 417, 448 |
| abstract_inverted_index.allows | 101 |
| abstract_inverted_index.areas. | 156 |
| abstract_inverted_index.caused | 3 |
| abstract_inverted_index.causes | 201 |
| abstract_inverted_index.derive | 310, 324 |
| abstract_inverted_index.effect | 197 |
| abstract_inverted_index.health | 6 |
| abstract_inverted_index.hidden | 141 |
| abstract_inverted_index.issues | 2 |
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| abstract_inverted_index.mainly | 35 |
| abstract_inverted_index.merged | 94 |
| abstract_inverted_index.mobile | 46 |
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| abstract_inverted_index.number | 235 |
| abstract_inverted_index.relies | 75 |
| abstract_inverted_index.remote | 112 |
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| abstract_inverted_index.values | 58 |
| abstract_inverted_index.(9341): | 368 |
| abstract_inverted_index.Aerosol | 81 |
| abstract_inverted_index.Balyan, | 455 |
| abstract_inverted_index.Holgate | 21 |
| abstract_inverted_index.Journal | 434 |
| abstract_inverted_index.Machine | 115 |
| abstract_inverted_index.Methods | 464 |
| abstract_inverted_index.Network | 406 |
| abstract_inverted_index.Optical | 82, 225 |
| abstract_inverted_index.Raymond | 414 |
| abstract_inverted_index.Reached | 430 |
| abstract_inverted_index.Sensing | 422, 477 |
| abstract_inverted_index.Stephen | 357 |
| abstract_inverted_index.account | 194 |
| abstract_inverted_index.aerosol | 8, 28, 176, 202, 269, 287, 314, 344, 350 |
| abstract_inverted_index.ambient | 218 |
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| abstract_inverted_index.complex | 144 |
| abstract_inverted_index.derived | 245 |
| abstract_inverted_index.growth. | 257 |
| abstract_inverted_index.in-situ | 41 |
| abstract_inverted_index.optical | 179, 318, 349 |
| abstract_inverted_index.related | 98 |
| abstract_inverted_index.sensors | 303 |
| abstract_inverted_index.studied | 155 |
| abstract_inverted_index.studies | 181, 209 |
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| abstract_inverted_index.variety | 71 |
| abstract_inverted_index.Counters | 227 |
| abstract_inverted_index.Holgate. | 359 |
| abstract_inverted_index.Learning | 116 |
| abstract_inverted_index.Low-Cost | 404 |
| abstract_inverted_index.Maibach, | 378 |
| abstract_inverted_index.Particle | 226, 234 |
| abstract_inverted_index.Promised | 432 |
| abstract_inverted_index.Quality: | 399 |
| abstract_inverted_index.Regional | 397 |
| abstract_inverted_index.Zongwei, | 445 |
| abstract_inverted_index.affected | 250, 266 |
| abstract_inverted_index.decades, | 52 |
| abstract_inverted_index.deriving | 56 |
| abstract_inverted_index.directly | 213 |
| abstract_inverted_index.humidity | 252 |
| abstract_inverted_index.low-cost | 301 |
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| abstract_inverted_index.particle | 255 |
| abstract_inverted_index.physical | 137, 165, 292 |
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| abstract_inverted_index.rigorous | 164 |
| abstract_inverted_index.thorough | 169 |
| abstract_inverted_index.together | 304 |
| abstract_inverted_index.& | 438 |
| abstract_inverted_index.Feenstra, | 380 |
| abstract_inverted_index.GeoHealth | 410 |
| abstract_inverted_index.Long-Term | 470 |
| abstract_inverted_index.Polidori, | 382 |
| abstract_inverted_index.Pollution | 362, 425 |
| abstract_inverted_index.Satellite | 408, 474 |
| abstract_inverted_index.approach, | 293 |
| abstract_inverted_index.auxiliary | 96 |
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| abstract_inverted_index.component | 284 |
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| abstract_inverted_index.estimated | 273 |
| abstract_inverted_index.humidity, | 199 |
| abstract_inverted_index.particles | 203 |
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| abstract_inverted_index.pollution | 9 |
| abstract_inverted_index.retrieval | 78 |
| abstract_inverted_index.satellite | 60, 86, 259, 306 |
| abstract_inverted_index.shortwave | 90 |
| abstract_inverted_index.sometimes | 140 |
| abstract_inverted_index.stations. | 47 |
| abstract_inverted_index.Brunekreef | 19 |
| abstract_inverted_index.California | 392 |
| abstract_inverted_index.Developing | 467 |
| abstract_inverted_index.Management | 440 |
| abstract_inverted_index.Pikelnaya, | 376 |
| abstract_inverted_index.Similarly, | 258 |
| abstract_inverted_index.algorithms | 260 |
| abstract_inverted_index.approaches | 117 |
| abstract_inverted_index.conditions | 189 |
| abstract_inverted_index.considered | 214 |
| abstract_inverted_index.definition | 333 |
| abstract_inverted_index.derivation | 162 |
| abstract_inverted_index.documented | 12 |
| abstract_inverted_index.estimation | 103 |
| abstract_inverted_index.innovative | 73 |
| abstract_inverted_index.layer.This | 288 |
| abstract_inverted_index.monitoring | 26 |
| abstract_inverted_index.procedures | 38 |
| abstract_inverted_index.properties | 319, 351 |
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| abstract_inverted_index.Doraiswamy, | 372 |
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| abstract_inverted_index.Statistical | 463 |
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| abstract_inverted_index.corrections | 192 |
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| abstract_inverted_index.information | 341 |
| abstract_inverted_index.investigate | 123 |
| abstract_inverted_index.possibility | 54, 296 |
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| abstract_inverted_index.theoretical | 332 |
| abstract_inverted_index.‘A | 460 |
| abstract_inverted_index.Christopher, | 449 |
| abstract_inverted_index.Christopher. | 419 |
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| abstract_inverted_index.measurements | 42, 183, 215, 237, 248, 299 |
| abstract_inverted_index.meteorology, | 100 |
| abstract_inverted_index.observations | 61 |
| abstract_inverted_index.reflectivity | 88 |
| abstract_inverted_index.relationship | 312 |
| abstract_inverted_index.standardized | 37 |
| abstract_inverted_index.wavelengths. | 91 |
| abstract_inverted_index.Nevertheless, | 135 |
| abstract_inverted_index.concentration | 107, 128, 186, 236, 315 |
| abstract_inverted_index.investigation | 170 |
| abstract_inverted_index.methodologies | 74 |
| abstract_inverted_index.relationships | 174 |
| abstract_inverted_index.specificities | 152 |
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| abstract_inverted_index.interpretation | 138 |
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| abstract_inverted_index.physico-chemical | 173 |
| abstract_inverted_index.‘Impact | 390 |
| abstract_inverted_index.‘Remote | 421 |
| abstract_inverted_index.Health’. | 364 |
| abstract_inverted_index.humidity-dependent | 325 |
| abstract_inverted_index.645–75Ma, | 444 |
| abstract_inverted_index.172–81Hoff, | 413 |
| abstract_inverted_index.locations.Statistical | 113 |
| abstract_inverted_index.1233–42Gupta, | 369 |
| abstract_inverted_index.Observations’. | 409 |
| abstract_inverted_index.simulations.References:Brunekreef, | 354 |
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| corresponding_author_ids | https://openalex.org/A5068255176, https://openalex.org/A5093735669, https://openalex.org/A5053500168, https://openalex.org/A5089333410, https://openalex.org/A5072041473, https://openalex.org/A5066206957 |
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| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I9360294 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
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| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.04339119 |
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| citation_normalized_percentile.is_in_top_10_percent | False |