Regional analysis of factors affecting visual air quality

Pitchford, Ann; Pitchford, Marc; Malm, William C.; Flocchini, Robert G.; Cahill, Thomas A.; Walther, Eric G.

doi:10.1016/0004-6981(81)90237-7

Cited by 37 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The U.S. Environmental Protection Agency (USEPA) Notice of Proposed Rulemaking (NPR) for regional haze uses the Deciview haze index (dv) as an indicator for visibility impairment [11,16]. A change of 1 dv corresponded to about a 10 % change in light extinction and is approximately constant under the assumption of atmospheric and landscape feature conditions [17].…”

Section: ) Quantification Of the Atmospheric Visibilitymentioning

confidence: 99%

PM10 and PM2.5 from Haze Smog and Visibility Effect in Chiang Mai Province Thailand

2018

View full text Add to dashboard Cite

Air pollution from haze smog in Chiang Mai Thailand has become a serious problem, with fine particulate matter (FPM), PM10and PM2.5, as the main culprits. These pollutants haveserious effects on health and affect visibility in transportation and tourism. In this study, reduction in visibility was monitored using a digital camera, video records and aerial photography. Visibility in Chiang Mai was analyzed using qualitative and quantitative methods. Visibility was directly measured by GPS and Google Earth mapping. Visibility reduction from haze events was also compared by image analysis in Deciview units. Fine particulate matter concentrations and frequency of fires in Chiang Mai were associated with visibility reduction. Forest fires increased Deciview numbers. In the dry season, the frequency of fire incidents was correlated with both PM10and PM2.5with R2= 0.9 (95% CI, p<0.05). The reversecorrelation (-R2) between visual length (km) and PM10and PM2.5were 0.64 and 0.72 at altitude444 m with 95% CI, p<0.05. The reverse correlation (-R2), at altitude 313 m was 0.93for PM10and 0.96 for PM2.5with 95% CI, p<0.05. The reverse correlation (-R2), at altitude 324 m was 0.86 for PM10 and 0.93 for PM2.5with 95% CI, p<0.05. The association between visibility and FPMat low altitude was found to be more significant than at high altitude.

show abstract

Section: ) Quantification Of the Atmospheric Visibilitymentioning

confidence: 99%

PM10 and PM2.5 from Haze Smog and Visibility Effect in Chiang Mai Province Thailand

2018

View full text Add to dashboard Cite

show abstract

“…The ATAD model has been used by many researchers to compute forward and backward air trajectories 2,10,11,12 using wind data from the national network of radiosonde stations. Because the model requires little computational time, a large number of trajectories can be run for statistical analyses.…”

Section: Atadmentioning

confidence: 99%

Evaluation of Wind Fields Used in Grand Canyon Visibility Transport Commission Analyses

Green

Pai

Ashbaugh

et al. 2000

Journal of the Air & Waste Management Association

View full text Add to dashboard Cite

The Grand Canyon Visibility Transport Commission (GCVTC) was established by the U.S. Congress to assess the potential impacts of projected growth on atmospheric visibility at Grand Canyon National Park and to make recommendations to the U.S. Environmental Protection Agency on what measures could be taken to avoid such adverse impacts. A critical input to the assessment tool used by the commission was three-dimensional modelderived wind fields used to transport the emissions. This paper describes the evaluation of the wind fields used at various stages in the assessment. Wind fields evaluated included those obtained from the Colorado State University Regional Atmospheric Modeling System (RAMS), the National Meteorological Center's Nested Grid Model (NGM), and the National Oceanic and Atmospheric Administration's Atmospheric Transport and Dispersion (ATAD) trajectory model. The model-derived wind fields were evaluated at multiple vertical levels at several locations in the southwestern United States by determining IMPLICATIONS Model winds used for the Grand Canyon Visibility Transport Commission assessment were not in good agreement with measured winds. The poor performance of the wind field models adds significant uncertainty to the quantification of source-receptor relationships used in the GCVTC assessment. Until wind fields are improved, considerable caution must be used when considering the results of air quality models in the highly complex terrain of the southwestern United States. differences between model predicted winds and winds that were measured using radiosonde and radar wind profiler data. Model-derived winds were also evaluated by determining the percent of time that they were within acceptable differences from measured winds.All models had difficulties, generally meeting the acceptable criteria for less than 50% of the predictions. The RAMS model had a persistent bias toward southwesterly winds at the expense of other directions, especially failing to represent channeling by north-south mountain ranges in the lower levels. The NGM model exhibited a substantial bias in the summer months by extending northwesterly winds in the eastern Pacific Ocean well inland, in contrast to the observed southwesterlies at inland locations. The simpler ATAD trajectory model performed somewhat better than the other models, probably because of its use of more upper air sites. The results of the evaluation indicated that these wind fields could not be used to reliably predict source-receptor impacts on a particular day; thus, seasonally averaged impacts were used in the GCVTC assessment.

show abstract

“…Pitchford et al 1 suggested the possibility of clean air corridors after back-trajectory analysis from the Grand Canyon revealed that six of the ten clearest days and none of the 10 haziest days were associated with trajectories coming from the north and northwest. White et al 2 classified back-trajectories from the Grand Canyon for 1988-1989 as coming from one of four quadrants (northwest, northeast, southwest, and southeast), or from a mixture of quadrants.…”

Section: Introductionmentioning

confidence: 99%