<|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:"Decreases in the urban heat island effect during the Coronavirus Disease 2019 (COVID-19) lockdown in Wuhan, China: Observational evidence."
Abstract:"The Coronavirus Disease 2019 (COVID-19) lockdown in early 2020 reduced human activities in Wuhan, China, thereby altering the urban heat island (UHI) effect. The epidemic period (EP) during January 1, 2020-May 31, 2020 is divided into four stages, including pre-lockdown, first (EP_LS1) and second (EP_LS2) lockdown, and after-lockdown (EP_ALS) stages, which are identified according to different antivirus measures in early 2020 and confirmed by comparing the daily electricity consumption over the Wuhan metropolitan area during 2020 and 2019. Then, the Wuhan UHI intensity (UHII) during each stage is compared to that during the baseline period of 2016-2019 and 2021. Daytime, night-time, and daily UHIIs are reduced; the larger decrease is during EP_LS1 and EP_LS2, of which EP_LS1 with the strictest antivirus measures shows a significant decrease (<-0.12 degrees C or <-36%), especially during night-time with -0.23 +/- 0.05 degrees C (or -47 +/- 9%). The declined UHII persists in all stages (excluding EP_ALS), even after removing the natural variability of the air temperature; EP_LS1 still exhibits the largest and significant decrease (<-0.18 degrees C or <-52%). To remove possible impacts of the local weather conditions, the UHII changes are recalculated by randomly sampling the 90, 80, 70, and 60% data points in each stage of EP with 1,000 repetitions. Results suggest limited influence of the local weather conditions, and then validate our findings. This study provides an important observational evidence of human-induced control on urban climate, and further confirms the necessity to include human dynamics in climate/earth system models for better simulating and predicting urban climate.."
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: 

Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

Decreases in the urban heat island effect during the Coronavirus Disease 2019 (COVID-19) lockdown in Wuhan, China: Observational evidence
The Coronavirus Disease 2019 (COVID-19) lockdown in early 2020 reduced human activities in Wuhan, China, thereby altering the urban heat island (UHI) effect. The epidemic period (EP) during January 1, 2020-May 31, 2020 is divided into four stages, including pre-lockdown, first (EP_LS1) and second (EP_LS2) lockdown, and after-lockdown (EP_ALS) stages, which are identified according to different antivirus measures in early 2020 and confirmed by comparing the daily electricity consumption over the Wuhan metropolitan area during 2020 and 2019. Then, the Wuhan UHI intensity (UHII) during each stage is compared to that during the baseline period of 2016-2019 and 2021. Daytime, night-time, and daily UHIIs are reduced; the larger decrease is during EP_LS1 and EP_LS2, of which EP_LS1 with the strictest antivirus measures shows a significant decrease (<-0.12 degrees C or <-36%), especially during night-time with -0.23 +/- 0.05 degrees C (or -47 +/- 9%). The declined UHII persists in all stages (excluding EP_ALS), even after removing the natural variability of the air temperature; EP_LS1 still exhibits the largest and significant decrease (<-0.18 degrees C or <-52%). To remove possible impacts of the local weather conditions, the UHII changes are recalculated by randomly sampling the 90, 80, 70, and 60% data points in each stage of EP with 1,000 repetitions. Results suggest limited influence of the local weather conditions, and then validate our findings. This study provides an important observational evidence of human-induced control on urban climate, and further confirms the necessity to include human dynamics in climate/earth system models for better simulating and predicting urban climate.