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Record W3135825307 · doi:10.9734/bpi/magees/v3/6516d

Characterisation of Air Pollution on Trinidad’s North-West Coast (San Fernando to Port-of-Spain)

2021· book-chapter· en· W3135825307 on OpenAlexaboutno aff
Himawatee Baboolal, Derrick A Balladin

Bibliographic record

VenueBook Publisher International (a part of SCIENCEDOMAIN International) · 2021
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsParticulatesEnvironmental scienceAir pollutionPollutionEnvironmental engineeringEnvironmental protectionGeography

Abstract

fetched live from OpenAlex

Air pollution is a complex mixture of toxic gases and particulates that has been identified as the largest global environmental threat facing the world today, estimated to have caused 7-10 million deaths worldwide annually [1,2]. Trinidad is the most industrialised of the Caribbean islands, with a large energy and petrochemical economic base. In addition, it is affected by seasonal Sahara dust (PM2.5). This study characterizes the baseline levels of fine and respirable particulates (three PM size fractions), trace metals in PM, gaseous pollutants and meteorological parameters at four sites over the heavily populated west coast of Trinidad during March’ 15 - May ‘16. Stations represent rural, urban, mixed background and industrial land uses. Annual mean levels of PM2.5 and PM10 in ambient air exceeded the WHO guidelines for protection of public health at all four stations (n=522). PM2.5 and PM10 exceed the WHO (2006) safe limit guidelines (PM2.5 is 10 µg/m3, PM10 is 20 µg/m3) over 70% of the time sampled at urban and industrial sites. Factor analysis indicated the variables impacting PM distribution and type of PM (size fraction) were time of year and location of station (land use). The temporal pattern for PM at the industrial station was markedly different from the other three stations, being at a constant high level throughout the year. Trace metals (in PM10) found in exceedance of Canadian (Ontario) 2012 [3] standards were, beryllium, cadmium, chromium, iron, manganese and nickel. Cd and Ni were deemed to pose the highest risk to public health as they are genotoxic carcinogens found in the smallest PM fractions. Several gaseous pollutants of concern were CO, NH3, NO2, N2O, C6H6. Nitrogen dioxide and benzene were the most prolific, being the highest most frequently at the industrial and urban stations. NO2 exceedance averaged 89% (often 2-3 times the USEPA limit for the protection of public health), reflective of the large amount of industrial combustion gases in the ambient air in the Pt. Lisas area. Benzene was found to exceed public health limit values >90% of the time measured at the urban station (Port-of-Spain) and at >80% at the mixed background station (at San Fernando). Both gases are reflective of combustion and vehicle emissions derived pollutant sources. The pollutant data was used to calculate and validate an aggregated Air Pollution Index (R2= 0.91) that could be readily applied to ongoing monitoring data in four statistically validated classification tiers; Good, Normal, High, Very High. The industrial station has the highest frequency of ‘very high’ pollutant levels, as well as the highest frequency of ‘good’ air quality days. The urban station had highest frequency of ‘normal’ to ‘high’ rankings. The rural station, as expected, had much better overall air quality. The worst air quality occurred during June-July ’15 and December ’15 -January ’16 periods. Monitoring data for air quality provides the best option to drive data driven decision making and effective air pollution management. The baseline levels of air pollutants provide adequate justification for revision of the current local regulations to afford better public health protection from unnecessarily high levels of ambient air pollution.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.284
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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