Ambient air pollution per specific land use types and activities in an urbanizing Eastern Caribbean Country, St. Kitts and Nevis

Air quality and urban development are inextricably linked. The proliferation of urban centers can have positive outcomes [1–3], such as economic growth [3–6]. However, urbanization, which drives urban projects due to the migration of persons from rural to urban areas, can also have negative impacts, such as degradation of ambient air quality [5, 7–10].

The adverse environmental impacts of urbanization on ambient air quality are typically modified through changes in land utilization. Urban development is associated with land use shifts away from agriculture toward transport [11–13], recreation [14] including tourism-based services [11, 14] and building projects [15–18]. Such transformations are then associated with a variety of airborne contaminants including criteria air pollutants such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃) and particulate matter (PM_2.5 and PM₁₀) [19–21].

Certain adverse health effects are uniquely linked to concentrations of NO₂ [22, 23], SO₂ [24–27], O₃ [28–31], and PM [32–36]. Asthma [37–39], bronchitis [40, 41], lung cancer [42–44] and over 4 million annual respiratory-related deaths globally [45, 46] are all common outcomes associated with urban air pollution.

However, while spatial and temporal features of urban air pollutants have been investigated and reported extensively in more developed and industrialized nations such as the US [47], Canada [48] and China [49, 50], less developed nations are often underexplored. Latin American and the Caribbean (LAC) are among the most urbanized regions on earth [51] with an estimated 100 million residents exposed to air pollution levels exceeding World Health Organization guidelines [51–53]. While locales such as Bogota (Colombia), Rio de Janeiro (Brazil), Santiago (Chile) and Jamaica have established air quality monitoring networks [51, 53], many Eastern Caribbean (EC) nations lack adequate air quality surveillance systems [53, 54].

St. Kitts and Nevis (SKN), a 261-km² two-island nation of approximately 55,000 people, is among the most urbanized countries in the EC. The urban population is approximately 32%; many development projects are occurring along the nation's roadways, replacing green spaces once earmarked for agriculture. Between the mid-1990s and early 2010s, the ratio of cars to people doubled, increasing to 2 cars for every per 5 persons [55]. Tourism, which demanded expansions in infrastructure and transport, has dominated as the major revenue-generating sector [56]. A revitalized citizen-by-investment-program (CBI), which earned the small twin-island nation a global rank of 16th for issuing construction permits [57], led to numerous building projects around both islands. Throughout this land-use transformation, SKN, like many other EC nations, has not established an air quality monitoring system.

As such, the following is the first multi-year study aimed to evaluate temporal and spatial variability in the concentration of atmospheric air pollutants, namely NO₂, O₃, SO₂ and PM, in the twin-island federation of St. Kitts and Nevis (SKN). Pollutant concentrations were measured at select sample sites through multiple, repeated, short-term sampling campaigns between October 2015 and November 2018. Air pollution estimates were then modeled against the various land use, temporal and meteorological features to identify factors that influence air pollutant concentrations.

Site selection

Local land use records (circa 2013) and in-field tours (2014 to 2015) were used to evaluate unique land use type profiles. Included in the document review was the evaluation of urbanicity, dichotomized as low or high, according to the limited versus notable presence of built environment, respectively. The types most associated with urban development were identified, specifically transport (including roadways, bus stops, parking lots, ports and other transportation hubs), recreation (including public parks, outdoor restaurants, shopping centers, open-air performance spaces) and construction (including moderate to large scale excavation, building, renovation, assembly of a physical dwellings and fixtures, demolition activities and preparation of building materials).

Using ArcGIS (v.10.22), a grid of 500 m × 500 m grid cells was applied to a digital map of St. Kitts and Nevis. Cells were then manually coded according to specific land use types. Monitor placement was thereafter determined via (a) stratified random sampling and (b) purposeful sampling.

With regard to stratified random sampling, 24 cells were chosen using a random number generator applied to all potential sampling sites, six transport candidates first followed by six for recreation and then six for construction. Purposeful sampling involved selecting sites as needed per previous studies of various airborne pollutants [22, 58] that would be (i) a minimum of 200 m from those previously chosen to enhance spatial variability, (ii) specific to an underrepresented land use type with the aim of obtaining at least 5 sites for each type, or (iii) considered to be unique sources (e.g. transport hubs that serviced vehicles of various types). A final selection of 27 was made; transport (n = 14), recreation (n = 7) and construction (n = 6), as shown in (figure 1). Twenty-one (21) of the selected sites were located on St. Kitts. Additionally, per historical records, approximately 30% of the sampling sites in St. Kitts were in the most urban areas, namely St. George, St. Kitts, St. Anne and St. Mary while in Nevis, only 1 of the 6 selected sites (Charlestown) was deemed high urban.

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Environmental monitoring

Analytic methods—data analysis

Pollution—descriptions and associations

The characteristics of the sampling sites are shown in table 1. Across 27 sites, mean NO₂, O₃, SO₂, PM₁₀ and PM_2.5 levels were 26.61 ppb (range: 0–306 ppb), 11.94 ppb (0–230 ppb), 27.9 ppb (0–700 ppb), 52.9 μg m⁻³ (0–10,400 μg m⁻³) and 29.8 μg m⁻³ (0–1556 μg m⁻³), respectively.

Table 1.  Land use characteristics of sampling sites in St. Kitts and Nevis.

						Area (m2)
Site rank	Parish	Island	Population	Land use type	Monitor from road (m)	Coast	Water body	Road	Vegetation	Built	SESa	High urban	Tourist activities
BAA	Saint George	St. Kitts	14000	Transport	0.2	1426.48	0.00	0.00	0.1	0.08	9	Yes	No
BAH	Saint George	St. Kitts	14000	Construction	6	1426.48	0.00	0.00	0.1	0.08	9	Yes	No
BTT	Saint George	St. Kitts	14000	Transport	0.75	271	0.00	0.12	0.02	0.11	9	Yes	Yes
BTY	Saint Anne	St. Kitts	3200	Transport	5	34.49	0.02	0.00	0.09	0.07	10	No	No
BOU	Christ Church	St. Kitts	3200	Construction	1	269.6	0.00	0.12	0.02	0.11	13	No	No
BIH	Saint Anne	St. Kitts	14000	Transport	1	819.73	0.00	0.00	0.25	0.00	10	No	No
BTL	Saint George	St. Kitts	2000	Transport	5	269.99	0.00	0.00	0.17	0.08	9	Yes	No
CYN	Saint Mary	St. Kitts	3500	Recreation	0.5	1323.4	0.00	0.02	0.13	0.08	11	Yes	No
CFB	Saint George	St. Kitts	14000	Transport	0.5	397.63	0.00	0.03	0.1	0.11	9	Yes	No
CHN	Saint Paul	Nevis	1800	Transport	5	20	0.01	0.00	0.08	0.06	6	Yes	Yes
CTC	Saint Peter	St. Kitts	3700	Construction	7	4	0.06	0.00	0.00	0.1	9	No	No
FRB	Saint George	St. Kitts	14000	Recreation	175	20	0.07	0.01	0.03	0.02	1	No	Yes
GNG	Saint George	Nevis	2600	Recreation	2	283.76	0.00	0.01	0.24	0.01	8	No	No
GOR	Saint George	St. Kitts	14000	Construction	5	10	0.05	0.01	0.01	0.05	1	Yes	No
INS	Saint George	St. Kitts	14000	Recreation	0.25	271	0.00	0.12	0.02	0.11	9	Yes	Yes
JNF	Saint George	St. Kitts	14000	Transport	0.5	1058.95	0.00	0.04	0.05	0.16	9	Yes	No
LFL	Saint George	St. Kitts	14000	Construction	0.5	266.45	0.15	0.00	0.02	0.01	1	No	No
MPR	Saint George	Nevis	2600	Recreation	700	2565.78	0.00	0.01	0.16	0.08	8	No	Yes
NEW	Saint James	Nevis	1800	Transport	1	1340	0.00	0.03	0.12	0.1	2	No	Yes
PZ	Saint George	St. Kitts	14000	Recreation	0.5	271	0.00	0.12	0.02	0.11	9	Yes	Yes
RLB	Saint Peter	St. Kitts	3700	Transport	100	5	0.05	0.01	0.01	0.06	9	Yes	Yes
RSU	Trinity	St. Kitts	1800	Transport	15	377.04	0.00	0.03	0.2	0.02	3	No	No
SBG	Saint Thomas	Nevis	3000	Transport	10	15	0.02	0.06	0.08	0.01	12	No	Yes
SN	Saint Thomas	St. Kitts	2500	Recreation	10	11	0.02	0.04	0.06	0.01	12	Yes	Yes
STJ	Saint James	Nevis	1800	Transport	5	1029.45	0.00	0.01	0.21	0.03	2	No	No
TTS	Saint Anne	St. Kitts	3200	Construction	1.75	15	0.03	0.00	0.06	0.05	10	Yes	No
WAH	Saint George	St. Kitts	14000	Transport	6.00	1426.48	0.00	0.00	0.10	0.08	9	Yes	No

^aSocioeconomic status ranking based on average resident income in Parish.

Pollutants were generally significantly positively correlated with other. The exception was PM₁₀, which was negatively correlated with all pollutants other than PM_2.5 (figure 2). Pollutant levels varied by location. NO₂, PM_2.5, and PM₁₀ profiles between the islands of St. Kitts and Nevis differed significantly (table 2). Mean concentrations for each pollutant varied significantly by sampling site (p < 0.0001) as shown in figure 3 (data in supplementary table A is available online at stacks.iop.org/ERC/2/041002/mmedia).

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Table 2.  Pollutant concentrations per island across sampling campaigns.

Pollutant (unit)	St. Kitts				Nevis
	n		max	s	n		max	s	p (t-test)
NO2 (ppb)	12168	27	306	26	713	23	81	17	<.0.0001
O3 (ppb)	8227	12	230	13	666	11	106	13	0.0313
SO2 (ppb)	2475	28	700	51					NA
PM10 (μg m−3)	7634	55	10400	338	528	25	248	28	<.0.0001
PM2.5 (μg m−3)	774	31	1556	89	130	16	103	17	<.0.0001

n–sample size; -sample mean; s-sample standard deviation.

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Sites designated as high urban had higher concentrations for all pollutants except for PM_2.5 (table 3). Locations that served as venues for tourist activities tended to have lower concentrations of NO₂ and SO₂ and PM_2.5, while PM₁₀ was significantly higher. O₃ did not differ according to tourist activity. With respect to land utilization, NO₂ and PM_2.5 averaged the highest levels at construction sites at 29 ppb (max = 306) and 68.0 μg m⁻³ (max = 1556), respectively. O₃ and PM₁₀ averaged the highest levels at recreational sites: 12.4 ppb (max = 203) and 86 μg m⁻³ (max = 10,400), respectively. Mean SO₂ concentrations were highest at transport sites, averaging 36 ppb (max = 700), while levels around recreational and constructions sites were much lower at 19 ppb and 11 ppb, respectively. All pollutant concentrations varied significantly across specific land use types (p < 0.0001). Differences are illustrated in figure 4. Average background concentrations for NO₂, O₃, SO₂, PM₁₀ and PM_2.5 were negligible; 2.4 ppb (0–37 ppb), 2.2 ppb (0–35 ppb), 3.1 ppb (1–7 ppb), 4.4 μg m⁻³ (0–290 μg m⁻³) and 4.4 μg m⁻³ (0–7 μg m⁻³) respectively.

Table 3.  Air pollutant concentrations per additional terrestrial traits and activities.

	NO2 (ppb)				O3 (ppb)				SO2 (ppb)				PM10 (μg m−3)				PM2.5 (μg m−3)
Traits/Activities		max	s	pa		max	s	pa		max	s	pa		max	s	pa		max	s	pa
Urban
Low	22	306	16	<0.0001	9.7	106	11	<0.0001	12	170	23	<0.0001	44	2070	109	0.049	35	582	119	<0.30
High	28	156	27		12.4	230	13		30	700	54		54	10400	352		27	1556	50
Tourist Activities
Yes	23	189	20	<0.0001	12	230	15	0.7000	20	320	32	<0.0001	81	10400	551	<0.0001	25	44	44	0.0801
No	28	306	28		12	106	12		31	700	57		37	3690	100		33	1556	100

^at-test.

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Time significantly affected air quality profiles in a number of ways (figure 5). NO₂, SO₂ and PM₁₀, concentrations tended to be higher in the afternoon. PM₁₀ levels were higher during non-commuter hours. All pollution levels were higher in the wet season with the exception of SO₂. Some pollutant concentrations were significantly different in the tourist (tour) season compared to other time periods. Levels were significantly higher for all pollutants except for PM_2.5, which was notably lower. Table 4 presents additional temporal differences: campaign, year, month, day of the week. Smoothed time-trend curves are shown in figure 6.

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Table 4.  Temporal characteristics and differences across air pollutants.

Temporal	NO2 (ppb)				O3 (ppb)				SO2 (ppb)				PM10 (μg m−3)				PM2.5 (μg m−3)
characteristics		max	s	group		max	s	group		max	s	group		max	s	group		max	s	group
Campaign/Timepoint
1sta	25.0	15	182	cde	8.2	7.9	28	e					13	19	122	b	1.1	1.4	5	b
2nd	42.0	23	125	a	9.3	10	106	e					11	33	302	b	3.5	5.1	38	b
3rd	16.0	15	105	f	12.3	11.9	62	d					48	113	2070	b	10.4	15.5	108	b
4th	18.0	18	97	f	12.6	11.9	39	d					41	55	922	b	20	4.8	36	ab
5th	26.0	28	273	cd	10.1	9.7	58	e					42	73	1262	b	49	143.3	1556	a
6th	24.0	26	306	de	4.6	7.4	50	f	27.2	46.1	240	a	41	123	3690	b	38.7	51.7	442	a
7th	28.0	40	289	bc	16.3	12.8	44	c	28	46.6	320	a	31	182	2870	b
8th	44.0	32	252	a	26.1	13.9	57	a	12.1	11.6	44	b	107	746	10400	a
9th	23.0	22	273	e	13.9	10.7	45	cd	6.6	4.8	16	b	28	68	922	b
10th	19.0	16	73	ef	11.8	9.7	63	de	33.5	40.3	370	a	154	684	6910	a
11th	30.0	25	254	b	21.9	8.6	35	b	30.7	65.9	700	a	55	51	257	b
12tha	22.0	18	113	e	12.3	21.2	230	d					33	14	79	b
p (F-test)	p < 0.0001				p < 0.0001				p < 0.0001				p < 0.0001				p < 0.0001
Day
Sundaya	28	16	129	ab	9.1	7.1	30	b					40	39	318	b	22	2.3	30	ab
Monday	27	22	273	ab	13.2	13	62	b	36.5	67.1	700	a	21	31	411	b	22	42.7	312	ab
Tuesday	22	26	252	b	10.3	11.5	58	b	13.4	31	240	c	32	90	2870	b	14	12.2	108	b
Wednesday	27	26	273	ab	11.2	15.4	230	a	26.9	53.7	470	b	112	633	10400	a	27	49.8	442	ab
Thursday	30	29	306	a	12	11.6	63	b	32.6	43	370	ab	40	64	1262	b	43	139.6	1556	a
Friday	29	30	289	ab	15.1	13.8	57	b	24.5	40.8	320	b	39	112	2070	b	39	91.6	582	ab
Saturdaya	24	15	129	ab	10	9.6	39	b	5.2	6.4	23	c	41	164	2870	b	19	4.8	35	ab
p (F-test)	p < 0.0001				p < 0.0001				p < 0.0001				p < 0.0001				p < 0.0001
Month
January	43	31	252	a	20.7	15	106	a	28	46.6	320	a	92.4	686	10400	a	4	5.9	38	c
April	17	16	105	d	12.3	11.9	62	b	9.5	7.9	28	b	45	98	2070	b	12.5	14.5	108	c
May	23	22	273	c	14.5	10.7	45	b	12.6	12.2	44	b	28.2	71	922	b
June	23	21	273	c	9.8	8.4	63	c	6.1	4.9	15	b	31.8	37	345	b	24.6	12.9	62	bc
July	25	31	306	bc	8.1	9.8	58	c	7	4.7	16	b	64.2	280	6910	ab	52	133.3	1556	a
August	25	20	139	bc	3.8	6.3	50	d					31.6	50	778	b	42.8	71	442	ab
October	27	20	254	b	13.5	10.5	35	b	33.5	40.3	370	a	51.5	50	257	b	1.1	1.4	5	c
Novembera	24	26	289	c	13.6	19	230	b	29.8	61.9	700	a	30.6	178	2870	b
p (F-test)	p < 0.0001				p < 0.0001				p < 0.0001				p < 0.0001				p < 0.012
Year
1-Y2015	25	15	182	b	8.2	7.9	28	c					9.7	18	147	b	1.6	1.5	5	b
2-Y2016	24	26	306	bc	9.1	10.6	106	c	27	46	240	a	40.2	112	3690	b	31.1	84.7	1556	a
3-Y2017	34	29	273	a	22.1	13	63	a	26	39	370	a	77	510	10400	a
4-Y2018	22	18	113	c	12.3	21.2	230	b	31	66	700	a	33.2	14	79	b
p (F-test)	p < 0.0001				p < 0.0001				0.008				p < 0.0001				0.0012

Different letters in group columns indicate significant differences (p < 0.05) per post-hoc Tukey tests. ^aSummary does not include data from all monitoring sites.

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Per an interim tour of sampling locations in June 2016, it was observed that construction projects were initiated in 26 of the 27 sites selected for sampling. The contribution of these activities could not be parsed despite attempts to log and analyze site and district-specific events. The parishes with the most construction projects proximate to sampling sites were St. Peter (n = 10), St. Anne (n = 12), St. Mary (13), St. George in St. Kitts (24) and St. Thomas in St. Kitts (26), all designated as urban except for the last.

Onsite researchers observed remnant plumes of Sahara Dust on July 6, 10 and 11 of 2016; the PM₁₀ concentrations on these three days averaged approximately 25.0 μg m⁻³ (0–169 μg m⁻³).

Meteorology

Traffic volume and other potential sources

Modelling

This air sampling study provided new information on a range of pollutants and their determinants in this understudied area of the Eastern Caribbean. It is the first of its kind in the nation of St. Kitts and Nevis, finding land use type, tourism season and to some lesser extent, drought, to be unique factors contributing to air pollutant concentration variability. Results offer insight for future exposure and health studies as well as context for policy makers regarding ambient air quality.

The higher concentrations of PM_2.5 and NO₂ at constructions sites could respectively be attributed to the movement and grading of building materials [16, 18] as well as the relative contributions of energy being used during building activities [59]. SO₂ levels likely peaked at transport sites due to the emissions profiles of various larger vehicles (e.g. passenger buses, taxi vans and diesel trucks) in spaces specifically configured for transport [60, 61], showing no difference across tour seasons as many of the sample locales would operate similarly year-round.

Previous studies showed urban areas tend to have more diminished air quality [62–64]. This increased risk of hazard has been framed as an environmental justice issue [65, 66] especially for those socioeconomically disadvantaged [63, 64, 67, 68]. In this investigation, most pollutant concentrations were elevated in high urban areas to varying degrees possibly owing to the selective action of the urban heat island (UHI) effect [69–71], short-term atmospheric conditions such as humidity and temperature [72] or long-term weather events such as drought [73]. Loss of significance due to urban levels (low versus high) was likely the result of mixed land use and configurations within varying urban form [74, 75].

The findings do suggest a general increase in ambient air pollution during tourist season as observed in other parts of the world [11, 76, 77].

Some social events, typically well-attended by SKN residents and visitors, did coincide with sampling schedules, most of them annual recreational affairs that are associated with increased traffic volumes as well as outdoor cook stove use [78] (e.g. St. Kitts' music festival, Nevis' Culturama and the St. Kitts and Nevis carnival celebrations held in June, August and December, respectively). That said, levels were significantly lower in areas where tourist activities take place, possibly as a result of transport between tourist sites being the major pollution. SN, an open-air recreational site undergoing renovations and known for cook stove use, was an exception with notably higher PM₁₀ and PM_2.5 levels, consistent with previous studies focused on the contributions of construction activities [16, 79] and smoke [80–82] to particulate matter concentrations.

With the exception of NO₂ which is likely bimodal in its AM-PM concentration distribution, pollutant levels trended higher in the later hours of the day (after 12 PM) aligning with previous studies as it relates to traffic pollution [83–85]. Diurnal significance would however diminish in constructed models.

The moderate drought conditions that persisted from late-2014 to mid-2017 likely depressed the difference between seasons for levels of particulate matter. PM_{10 concentrations} may have been lower in the designated wet season if not for the dry spell, as precipitation has been associated with suppression of particulate and some gaseous air pollutants [86–88]. The effect of rainfall deficits on air pollution levels aligns with observations and predictions of previous studies that indicate some association between climate change effects such as drought and air quality [89–92].

Ports and populated coastal areas with heavy ship traffic can be exposed to particulate emissions from marine vessels [93]. However, the convoy of terrestrial transport vehicles (e.g. buses and taxis) that would congregate at such sites (e.g. PZ) were likely key contributors to inland traffic pollution as previously observed [94]. Alternatively, various atmospheric factors could have impacted PM₁₀ [13], which seems evident in cases where traffic did not significantly factor into concentration models.

Average Sahara Dust PM₁₀ concentration was significantly lower than the study mean of 49.6 μg m⁻³. This could be the result of low deposition, as the Sahara Air Layer (SAL) is typically bounded between altitudes 1.5 and 3.7 km above sea level [95], well beyond the highest point in SKN (Mount Liamuiga) at 1.16 km. However, three days of data might not serve as sufficiently informative.

The constructed multivariate models had notably variable prediction capacities, only one with R² higher than 70%. Their usefulness could be constrained for a number of reasons including but not limited to the absence of unknown yet critical explanatory variables, the complexity of land use activities within specific land utilization types of interest, the uncaptured residual effect of other environmental events such as heavy precipitations between campaigns, or simply limited data across spatiotemporal scales. The notable poor performance of untransformed PM₁₀ models (R² less than 10%) is additionally likely due to the high impact of the SN site (Saint Thomas in St. Kitts). Logarithmic transformation of air pollutant concentrations, an approach conveniently and effectively applied in previous air quality studies [96, 97], potentially overcame some of aforementioned constraints.

It is important to note that all final analyses presented herein included potential outliers. The presence of these higher, possibly overly influential values, did impact the significance of certain covariates. Applying the outlier replacement procedure outlined in the methods did yield slightly stronger correlations among all pollutants, a few notably different covariates in constructed models for SO₂, PM₁₀ and PM_2.5, and slightly higher R² values for models for non-gaseous pollutants. However, we resolved that opting to remove this data risks sacrificing critical site-specific information and generating interpretations based on truncated data arguably more misleading than interpretations using the original data with potential outliers. Maximum values (e.g. 10.4 mg m⁻³ for PM₁₀ and 1.6 mgm⁻³ for PM_2.5) are short-term peaks and should not be construed as representing the long-term air quality of the region.

There are a few limitations to the study. First, not all pollutants were measured across all sites, on all days, and across multiple monitoring periods due to issues with site access and functional monitors. As such, some temporal summaries, namely descriptive statistics, across certain days, months and campaigns could not be generated, most notably for SO₂ and PM_2.5. On average, five out of the seven weekdays, generally Monday to Friday, were evaluated for pollution at each site. Overall, the analysis of these pollutants still have utility with regard to the profile of local air quality even if constrained by missing-ness per the aims of determining associations with land use features.

Second, in terms of design, the air quality estimates herein were based on short-term ambient air monitoring with handheld devices and no fixed monitoring sites with which to complement or compare. However, the portable devices employed in this study have been found to be sufficiently and reliably correlated with other established ambient air surveillance equipment [98–100]. Third, researchers did not attempt 24-h surveillance. Nevertheless, the 6:30 AM to 6:30 PM interval (a) was sufficient to evaluate pollution per land use type as well as other sources, (b) most comfortably facilitated the most efficient use of the portable monitors, each having limited batteries requiring daily recharge (c) helped to ensure the safety of onsite study personnel operating sampling devices and (d) is most relevant to etiology as this is the time that the majority of the population would tend to initiate, undertake and complete daily outdoor activities. Moreover, background concentrations suggest that the 12 hours not accounted for could have been negligible or at least very low.

Fourth, in reality, air pollution is dynamic and subject to migrations across local boundaries [101, 102]. As such, constraining air quality estimates spatially, based on land use across such small landmasses with mixed and changing characteristics might not be ideal. That said, the stratified and purposeful random sampling method ensured increased spatial coverage across research sites. Additionally, ad hoc adjustments for social activities, construction projects and extreme events generally did little to alter the significance in differences between categories in various analysis.

Finally, some districts (such as St. Mary, Trinity and St. Thomas, Nevis) were only represented by a single site while others contained many (e.g. St. George, St. Kitts). This might have resulted in parish districts being determined as more significant predictors of pollutions estimates than land use type. However, as this could not be reconciled without removing key sample sites, it seemed prudent to proceed with analyses that involve the complex mixture of land use types that are characteristic of SKN and many other Eastern Caribbean parish districts. While beyond the scope of this investigation, future studies should consider the heterogeneity within districts.

There are however notable strengths to this investigation as well. Given absence of spatial datasets for this understudied region, the creative use of multiple data resources, specifically the combination of GIS, local records and onsite characterizations, was vital to reducing misclassification of land use types and so category-specific concentration estimates. Additionally, investigations such as this one underscore the importance of micro-scale studies in developing tropical locales when engaging air pollution dynamics in smaller landmasses. Furthermore, this study evaluated multiple pollutants per their sources, interactions and impacting factors, both addressing a dearth of air pollution research in small island developing states and contributing to the wider global literature regarding spatiotemporal aspects of ambient air quality. Lastly, this probe of five target pollutants, which are considered to be criteria pollutants by the USEPA and important per WHO guidelines, can facilitate in the future, better quantification of health risk factors, offer critical information to epidemiological researchers and inform policymakers designing interventions, particularly in the EC region.

Overall, this study contributes to evidence of air pollution in small developing nations and provides some of the first systematic, albeit limited, monitoring in the Eastern Caribbean where surveillance systems are not yet established. Additionally, results offer insight into the change in air quality as a result of urban development features and at least one profound extreme weather event, specifically drought. Significance of land use types was generally limited to bivariate analysis, yet specific land use features, time and meteorology proved important factors in relation to air pollution levels. Further investigation is warranted with respect to pollutant spatiotemporal dynamics in the Eastern Caribbean setting, especially during intervals that would provide opportunity to compare air quality during non-drought periods to the pollutant concentrations recorded in this study. Estimating exposure and disease burdens relative to the pollution levels and accordingly risk categories is currently being explored in research carried out by some authors of this paper in order to determine the degree to which the air quality in this understudied region indeed might have etiological relevance.

The authors would like to acknowledge the financial support of the Yale Institute for Biospeheric Science (YIBS) in preliminary fieldwork source of funding for your research; the technical support of the Bell Research Group; the administrators of the Yale School of Public Health (YSPH), including but not limited to Dr Krystal Pollitt, Dr Robert Dubrow and Dr Brian Leaderer for their contributions to the development of the manuscript; administrators and students of the Clarence Fitzroy Bryant College (CFBC) in St. Kitts, especially students of the Environmental Science and Chemistry Departments who contributed to fieldwork; the SKN Ministries of Planning & Sustainable Development for the use of local land use records; the St. Kitts and Nevis Meteorological Offices for provision of weather data; other local partners; my relatives, friends and partners in SKN; the journal reviewers and editors of this manuscript.