Question

pm2.5 concentrations and smoke exposure duration by proximity to air quality system monitoring stations
researchers at colorado state university analyzed wildfire smoke impacts on air quality across the continental united states from 2007 to 2018, comparing populations living near environmental protection agency air quality system (aqs) monitoring stations to those in areas without nearby monitoring. the team measured both total fine particulate matter (pm2.5) concentrations and smoke - specific pm2.5. they defined a “smoke impact day” as any day when wildfire smoke contributed measurably to local pm2.5 levels. lead researcher dr. emily fischer hypothesized that existing monitoring networks underestimate smoke exposure for populations in less urbanized areas, since most aqs stations are located in cities. the team’s findings, she argued, reveal significant disparities in how different populations experience wildfire smoke pollution.
12 mark for review
which choice best describes data from the graph that support dr. fischer’s hypothesis?
a total pm2.5 concentrations were higher near aqs stations than in areas without monitoring coverage, even though smoke impact days were less frequent in parts of cities closer to aqs stations compared parts of cities farther away.
b although total pm2.5 concentrations were lower in areas away from aqs stations, smoke - specific pm2.5 and the number of smoke impact days were both higher in those areas.
c smoke pm2.5 concentrations were nearly identical regardless of proximity to monitoring stations, while smoke impact days positively correlated with distance from aqs stations.
d areas away from aqs stations had fewer smoke impact days than monitored locations, even though they showed higher levels of total pm2.5 compared to areas near aqs stations.

Answer:

To solve this, we analyze each option against the graph and hypothesis:

• Option A: Total PM2.5 near AQS is ~10, away is ~8 (not higher), so A is wrong.
• Option B: Total PM2.5 away is lower? No, away (black) for total is ~8 vs near (gray) ~10? Wait, no—wait the graph: Total PM2.5 (gray: near, black: away) – near is ~10, away is ~8? Wait no, first bar: Total PM2.5, gray (near) ~10, black (away) ~8? Wait no, second bar: Smoke PM2.5, gray ~0, black ~0.5? Third bar: Smoke Impact Days, gray ~18, black ~25. Wait, Dr. Fischer's hypothesis: monitoring underestimates smoke exposure in less urban (away from AQS) areas. So we need data showing away from AQS has more smoke impact or smoke PM2.5, even if total PM2.5 is similar or lower? Wait let's re-express the graph:

• Total PM2.5: Near (gray) ~10, Away (black) ~8 (so near is higher? Wait no, maybe I misread. Wait the y-axis: first bar (Total PM2.5) – gray (near) is ~10, black (away) is ~8. Second bar (Smoke PM2.5): gray ~0, black ~0.5. Third bar (Smoke Impact Days): gray ~18, black ~25.

Now, Dr. Fischer's hypothesis: monitoring (AQS, in cities) underestimates smoke exposure in less urban (away from AQS) areas. So "smoke exposure" would be smoke PM2.5 or smoke impact days. Let's check each option:

• Option A: "Total PM2.5 higher near AQS" – but total PM2.5 near is ~10, away ~8 (so near is higher). But "smoke impact days less frequent near AQS" – smoke impact days near (gray) ~18, away (black) ~25. So near has less smoke impact days (18 vs 25) but total PM2.5 higher near. But the hypothesis is about underestimating smoke exposure in away areas. So A says total PM2.5 higher near (which matches graph: near ~10, away ~8), and smoke impact days less near (near 18, away 25 – so near has less). But does this support underestimation? If near has higher total PM2.5 but less smoke impact days, maybe non-smoke PM2.5 is higher near. But the hypothesis is about smoke exposure. So A's logic: even though smoke impact days are less near AQS, total PM2.5 is higher near. But that doesn't support underestimating smoke in away areas.

• Option B: "Total PM2.5 lower away from AQS" – but away (black) total PM2.5 is ~8, near (gray) ~10 (so away is lower). Then "smoke-specific PM2.5 and smoke impact days higher in away areas". Smoke PM2.5: away (black) ~0.5, near (gray) ~0 (so away is higher). Smoke impact days: away ~25, near ~18 (so away is higher). So this says total PM2.5 is lower away, but smoke PM2.5 and smoke impact days are higher away. That would mean that in away areas, even though total PM2.5 is lower, the smoke-related exposure (smoke PM2.5 and impact days) is higher – so monitoring in AQS (near) would miss this higher smoke exposure in away areas (since AQS is in cities, monitoring near, but away has more smoke exposure). This matches the hypothesis: monitoring underestimates smoke exposure in away areas (because away has more smoke impact days and smoke PM2.5, even if total PM2.5 is lower). Wait but total PM2.5: near is ~10, away ~8 (so away is lower). So option B says "total PM2.5 lower away, but smoke-specific PM2.5 and smoke impact days higher away" – that fits: away has less total PM2.5 (maybe from other sources) but more smoke-related exposure, so AQS (monitoring near) would underestimate smoke exposure in away areas.

• Option C: "Smoke PM2.5 nearly identical" – but smoke PM2.5 near is ~0, away ~0.5 (not identical). So C is wrong.

• Option D: "Away from AQS had fewer smoke impact days" – but smoke impact days away (25) vs near (18) – away has more, so D is wrong (says fewer).

Wait, maybe I misread the total PM2.5. Wait the first bar: Total PM2.5 (μg/m³) – gray (near) is ~10, black (away) is ~8. So near has higher total PM2.5. Smoke PM2.5: near ~0, away ~0.5. Smoke Impact Days: near ~18, away ~25.

Now, Dr. Fischer's hypothesis: existing monitoring networks (AQS, in cities) underestimate smoke exposure for populations in less urbanized areas (away from AQS). So "smoke exposure" is smoke PM2.5 or smoke impact days. So we need data showing that away from AQS, smoke PM2.5 or smoke impact days are higher, even if total PM2.5 is not higher (or lower). Let's re-express each option:

• Option A: Total PM2.5 higher near AQS (matches: near ~10, away ~8). Smoke impact days less frequent near AQS (near ~18, away ~25 – so near has less). So A says "even though smoke impact days were less frequent near AQS, total PM2.5 was higher near". But the hypothesis is about underestimating smoke exposure in away areas. So if near has higher total PM2.5 but less smoke impact days, maybe non-smoke PM2.5 is higher near. But A's statement is "Total PM2.5 concentrations were higher near AQS stations than in areas without monitoring coverage, even though smoke impact days were less frequent in parts of cities closer to AQS stations compared parts of cities farther away." So near has higher total PM2.5, but fewer smoke impact days (since near is ~18, away ~25 – so near has less). So this would mean that near AQS (cities) have higher total PM2.5 (maybe from other sources) but less smoke exposure (fewer smoke impact days), while away from AQS (suburban/rural) have lower total PM2.5 but more smoke exposure (more smoke impact days). But does this support the hypothesis that monitoring underestimates smoke exposure in away areas? If away areas have more smoke impact days (more smoke exposure) but lower total PM2.5, then AQS (monitoring total PM2.5) would miss the high smoke exposure in away areas (because total PM2.5 is lower there, but smoke exposure is higher). Wait, but option A says "total PM2.5 higher near AQS" (which is true: 10 vs 8), and "smoke impact days less frequent near AQS" (true: 18 vs 25). So A's logic: even though smoke impact days are less near AQS, total PM2.5 is higher near. But the hypothesis is that monitoring underestimates smoke exposure in away areas. So if away areas have more smoke impact days (more smoke exposure) but lower total PM2.5, then AQS (which measures total PM2.5) would think away areas have lower pollution, but actually their smoke exposure is higher. But option A is saying total PM2.5 is higher near, which is true, but does that support the hypothesis?

Wait maybe I made a mistake in the graph reading. Let's re-express the graph with correct labels:

• X-axis: Total PM2.5 (μg/m³), Smoke PM2.5 (μg/m³), Smoke Impact Days (annual avg)
• Y-axis: value (units: μg/m³ for first two, days for third)
• Colors: Gray = Near AQS (urban), Black = Away from AQS (suburban/rural)

So:

1. Total PM2.5:

• Near (gray): ~10 μg/m³
• Away (black): ~8 μg/m³

→ Near has higher total PM2.5.

1. Smoke PM2.5:

• Near (gray): ~0 μg/m³
• Away (black): ~0.5 μg/m³

→ Away has higher smoke PM2.5.

1. Smoke Impact Days:

• Near (gray): ~18 days
• Away (black): ~25 days

→ Away has more smoke impact days.

Now, Dr. Fischer's hypothesis: "existing monitoring networks underestimate smoke exposure for populations in less urbanized areas, since most AQS stations are located in cities." So "smoke exposure" is smoke PM2.5 or smoke impact days. So we need data showing that away from AQS (less urban) has higher smoke exposure (smoke PM2.5 or smoke impact days) than near AQS (urban), even if total PM2.5 is lower (or similar) in away areas.

Let's analyze each option:

• Option A: "Total PM2.5 higher near AQS" (true: 10 vs 8). "Smoke impact days less frequent near AQS" (true: 18 vs 25). So A says near has higher total PM2.5 but fewer smoke impact days. But the hypothesis is about underestimating smoke exposure in away areas. If away areas have more smoke impact days (more smoke exposure) but lower total PM2.5, then AQS (monitoring total PM2.5) would miss the high smoke exposure in away areas. But option A's statement is "Total PM2.5 concentrations were higher near AQS stations than in areas without monitoring coverage, even though smoke impact days were less frequent in parts of cities closer to AQS stations compared parts of cities farther away." So this is saying that near AQS (cities) have higher total PM2.5 (maybe from non-smoke sources) but fewer smoke impact days, while away from AQS (suburban/rural) have lower total PM2.5 but more smoke impact days. Does this support the hypothesis? The hypothesis is that monitoring (AQS) underestimates smoke exposure in away areas. If away areas have more smoke impact days (more smoke exposure) but lower total PM2.5, then AQS (which measures total PM2.5) would not capture the high smoke exposure in away areas (because total PM2.5 is lower there, so they might think pollution is lower, but smoke exposure is higher). But option A's first part is "Total PM2.5 higher near AQS" – which is true, but does that support the hypothesis? Maybe not directly. Let's check other options.

• Option B: "Although total PM2.5 concentrations were lower in areas away from AQS stations, smoke-specific PM2.5 and the number of smoke impact days were both higher in those areas." Let's verify:
• "Total PM2.5 lower away from AQS": Away (8) vs Near (10) – true.
• "Smoke-specific PM2.5 higher away": Away (0.5) vs Near (0) – true.
• "Number of smoke impact days higher away": Away (25) vs Near (18) – true.

This matches the graph: away has lower total PM2.5 but higher smoke PM2.5 and more smoke impact days. This supports the hypothesis: AQS (in cities, near AQS) monitors total PM2.5, which is higher near, but smoke exposure (smoke PM2.5 and impact days) is higher away. So AQS would underestimate smoke exposure in away areas (since they look at total PM2.5, which is lower there, but smoke exposure is higher). So B seems correct.

• Option C: "Smoke PM2.5 concentrations nearly identical" – but smoke PM2.5 near is ~0, away ~0.5 – not identical. So C is wrong.

• Option D: "Areas away from AQS stations had fewer smoke impact days" – but away has 25, near 18 – so away has more, not fewer. So D is wrong.

Wait, earlier I thought option A's total PM2.5 was higher near, which is true, but option B's total PM2.5 is lower away, which is also true (8 vs 10). So option B's "total PM2.5 lower away" is correct, and "smoke-specific PM2.5 and smoke impact days higher away" is also correct. So B matches the graph and supports the hypothesis.

Wait but let's recheck option A: "Total PM2.5 concentrations were higher near AQS stations than in areas without monitoring coverage, even though smoke impact days were less frequent in parts of cities closer to AQS stations compared parts of cities farther away." So "smoke impact days were less frequent near AQS" – near has 18, away 25 – so near is less frequent (18 < 25) – that's true. "Total PM2.5 higher near" – 10 > 8 – true. But does this support the hypothesis? The hypothesis is that monitoring underestimates smoke exposure in away areas. If near has higher total PM2.5 (maybe from non-smoke sources) but fewer smoke impact days, while away has lower total PM2.5 but more smoke impact days, then AQS (monitoring total PM2.5) would see near as more polluted (higher total PM2.5) but miss the higher smoke exposure in away areas (since away has lower total PM2.5 but higher smoke impact days and smoke PM2.5). So both A and B seem to have parts that match, but let's check the numbers again.

Wait total PM2.5: near (gray) ~10, away (black) ~8. So near is higher. Smoke impact days: near ~18, away ~25. So near has fewer smoke impact days. So option A says: total PM2.5 higher near, smoke impact days less frequent near. Option B says: total PM2.5 lower away, smoke PM2.5 and smoke impact days higher away.

Now, the hypothesis is about "underestimating smoke exposure" in away areas. Smoke exposure is smoke PM2.5 or smoke impact days. So if away areas have higher smoke PM2.5 and more smoke impact days (higher smoke exposure) but lower total PM2.5, then AQS (which measures total PM2.5) would think away areas have lower pollution (since total PM2.5 is lower) but actually their smoke exposure is higher – so monitoring underestimates smoke exposure in away areas. This is exactly what option B says: total PM2.5 lower away, but smoke-specific PM2.5 and smoke impact days higher away. So B is correct.

Wait but earlier I thought option A's total PM2.5 was higher near, which is true, but option A's "smoke impact days less frequent near" is also true. But does option A support the hypothesis? The hypothesis is that monitoring (AQS) underestimates smoke exposure in away areas. If near has higher total PM2.5 (maybe from non-smoke) but fewer smoke impact days, and away has lower total PM2.5 but more smoke impact days, then AQS (looking at total PM2.5) would not capture the high smoke exposure in away areas. But option A's statement is "Total PM2.5 concentrations were higher near AQS stations than in areas without monitoring coverage, even though smoke impact days were less frequent in parts of cities closer to AQS stations compared parts of cities farther away." So this is saying that near AQS (cities) have higher total PM2.5 (maybe from other sources) but fewer smoke impact days, while away from AQS (suburban/rural) have lower total PM2.5 but more smoke impact days. This could imply that AQS (monitoring total PM2.5) is capturing non-smoke pollution in cities, but missing the smoke pollution in away areas (since away has more smoke impact days but lower total PM2.5). So both A and B seem to have elements, but let's check the smoke PM2.5.

Smoke PM2.5: near is ~0, away is ~0.5. So smoke-specific PM2.5 is higher away. Option B says "smoke-specific PM2.5... higher in those areas" (away) – which is true. Option A doesn't mention smoke-specific PM2.5, only total PM2.5 and smoke impact days. The hypothesis is about "smoke exposure", which includes smoke-specific PM2.5 and smoke impact days. So option B includes both smoke-specific PM2.5 and smoke impact days being higher away, which better supports the hypothesis that smoke exposure is underestimated in away areas (since both…