<|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:"Classification and transformation of aerosols over selected Indian cities during reduced emissions under Covid-19 lockdown."
Abstract:"Studies in the recent past show improved air quality over India during the Covid-19 lockdown. This research attempts to characterize atmospheric aerosols in terms of alpha and AOD and their transformation over India during the pandemic lockdown. The type and particle distribution of aerosols, including gaseous species for five Indian regions were considered. Fine to coarse particle shift was observed in most regions. The northern region observed high fire counts, implying crop residue burning season during the stringent lockdown. Thiruvananthapuram, in the south, showed an increase in PM, owing to the resumption of mobility post-lockdown. Hyderabad, however; observed increased PM2.5 (2.79%) and AOD (37.23%) during Phase 1. Maritime (MT) aerosol predominated over Thiruvananthapuram, whereas urban/biomass burning (UBB) type decreased over the eastern region. Contributions from continental average (CA), maritime continental average (MCA), and MT were observed over Hyderabad, post-lockdown. In the central region, MCA was replaced by UBB and mixed type, with isolated episodes of clean continental (CC) and desert dust (DD). During lockdown phases, an increase in O-3 over western, northern, and central regions is attributed to increased temperature and decreased NO2. A significant correlation with population density (PD) exists with NO2 (R-2 = 0.75; p < 0.05), suggesting human mobility as a major contributor to NO2 in the atmosphere during the lockdown period.."
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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: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [2];
Q3 = [1];
Q4 = [12];
Q5 = [];
Q6 = [1].
Classification and transformation of aerosols over selected Indian cities during reduced emissions under Covid-19 lockdown
Studies in the recent past show improved air quality over India during the Covid-19 lockdown. This research attempts to characterize atmospheric aerosols in terms of alpha and AOD and their transformation over India during the pandemic lockdown. The type and particle distribution of aerosols, including gaseous species for five Indian regions were considered. Fine to coarse particle shift was observed in most regions. The northern region observed high fire counts, implying crop residue burning season during the stringent lockdown. Thiruvananthapuram, in the south, showed an increase in PM, owing to the resumption of mobility post-lockdown. Hyderabad, however; observed increased PM2.5 (2.79%) and AOD (37.23%) during Phase 1. Maritime (MT) aerosol predominated over Thiruvananthapuram, whereas urban/biomass burning (UBB) type decreased over the eastern region. Contributions from continental average (CA), maritime continental average (MCA), and MT were observed over Hyderabad, post-lockdown. In the central region, MCA was replaced by UBB and mixed type, with isolated episodes of clean continental (CC) and desert dust (DD). During lockdown phases, an increase in O-3 over western, northern, and central regions is attributed to increased temperature and decreased NO2. A significant correlation with population density (PD) exists with NO2 (R-2 = 0.75; p < 0.05), suggesting human mobility as a major contributor to NO2 in the atmosphere during the lockdown period.