<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Impacts of distinct travel behaviors on potential air pollution exposure measurement error."
Abstract:"Prior studies typically estimate air pollution exposure at places of residence only; however, people are likely to conduct daily activities at out-of-home locations. Overlooking human mobility in exposure estimation can introduce measurement error in health analyses and may lead to ineffective public policies. We simulated travel mobility patterns for 100,784 residents of Los Angeles County and their exposure to outdoor PM2.5 on a typical weekday. We used K-means clustering via principal components analysis (PCA) to subdivide subjects into seven travel behavior clusters and compared their mobility-based dynamic PM2.5 exposures to their residence-based static exposure using our previously developed spatiotemporal model. Travel behavior correlated with expo-sure to outdoor PM2.5 and with the degree of PM2.5 exposure measurement error. Workers with long commuting distance (and highest income) had the lowest dynamic PM2.5 exposure (8.1 mu g/m3), while workers who relied on public transit (and were generally lower income) had the highest exposure (8.8 mu g/m3). In general, lower income individuals lived in more polluted neighborhoods and took longer duration (but shorter distance) commutes to work locations with similarly elevated PM2.5 concentrations, while higher income individuals took longer dis-tance commutes (similar duration) from less polluted residential to more polluted occupational neighborhoods. Worker and frequent traveler clusters with highest mobility had the highest exposure measurement error, and the non-worker stay-at-home cluster and student clusters had the lowest. The extent of exposure measurement error generally correlated with pollution levels at the residential location and distance (less so duration) of travel away from the residential neighborhood. Our results contribute to the literature on effects of travel behavior on exposure measurement error. The generated activity clusters combined with sociodemographic information provide additional information for researchers to understand which groups of people may be more challenging to model or predict their exposures accurately.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
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Impacts of distinct travel behaviors on potential air pollution exposure measurement error
Prior studies typically estimate air pollution exposure at places of residence only; however, people are likely to conduct daily activities at out-of-home locations. Overlooking human mobility in exposure estimation can introduce measurement error in health analyses and may lead to ineffective public policies. We simulated travel mobility patterns for 100,784 residents of Los Angeles County and their exposure to outdoor PM2.5 on a typical weekday. We used K-means clustering via principal components analysis (PCA) to subdivide subjects into seven travel behavior clusters and compared their mobility-based dynamic PM2.5 exposures to their residence-based static exposure using our previously developed spatiotemporal model. Travel behavior correlated with expo-sure to outdoor PM2.5 and with the degree of PM2.5 exposure measurement error. Workers with long commuting distance (and highest income) had the lowest dynamic PM2.5 exposure (8.1 mu g/m3), while workers who relied on public transit (and were generally lower income) had the highest exposure (8.8 mu g/m3). In general, lower income individuals lived in more polluted neighborhoods and took longer duration (but shorter distance) commutes to work locations with similarly elevated PM2.5 concentrations, while higher income individuals took longer dis-tance commutes (similar duration) from less polluted residential to more polluted occupational neighborhoods. Worker and frequent traveler clusters with highest mobility had the highest exposure measurement error, and the non-worker stay-at-home cluster and student clusters had the lowest. The extent of exposure measurement error generally correlated with pollution levels at the residential location and distance (less so duration) of travel away from the residential neighborhood. Our results contribute to the literature on effects of travel behavior on exposure measurement error. The generated activity clusters combined with sociodemographic information provide additional information for researchers to understand which groups of people may be more challenging to model or predict their exposures accurately.