<|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:"Non-negligible contributions to human health from increased household air pollution exposure during the COVID-19 lockdown in China."
Abstract:"Background: Ambient and household air pollution are found to lead to premature deaths from all-cause or cause-specific death. The national lockdown measures in China during COVID-19 were found to lead to abrupt changes in ambient surface air quality, but indoor air quality changes were neglected. In this study, we aim to investigate the impacts of lockdown measures on both ambient and household air pollution as well as the short-term health effects of air pollution changes. Methods: In this study, an up-to-date emission inventory from January to March 2020 in China was developed based on air quality observations in combination with emission-concentration response functions derived from chemical transport modeling. These emission inventories, together with the emissions data from 2017 to 2019, were fed into the state-of-the-art regional chemistry transport model to simulate the air quality in the North China Plain. A hypothetical scenario assuming no lockdown effects in 2020 was also performed to determine the effects of the lockdown on air quality in 2020. A difference-to-difference approach was adopted to isolate the effects on air quality due to meteorological conditions and long-term decreasing emission trends by comparing the PM2.5 changes during lockdown to those before lockdown in 2020 and in previous years (2017-2019). The short-term premature mortality changes from both ambient and household PM2.5 changes were quantified based on two recent epidemiological studies, with uncertainty of urban and rural population migration considerations. Findings: The national lockdown measures during COVID-19 led to a reduction of 5.1 mu g m(-3) in ambient PM2.5 across the North China Plain (NCP) from January 25th to March 5th compared with the hypothetical simulation with no lockdown measures. However, a difference-to-difference method showed that the daily domain average PM2.5 in the NCP decreased by 9.7 mu g m(-3) between lockdown periods before lockdown in 2020, while it decreased by 7.9 mu g m(-3) during the same periods for the previous three-year average from 2017 to 2019, demonstrating that lockdown measures may only have caused a 1.8 mu g m(-3) decrease in the NCP. We then found that the integrated population-weighted PM2.5, including both ambient and indoor PM2.5 exposure, increased by 5.1 mu g m(-3) during the lockdown periods compared to the hypothetical scenario, leading to additional premature deaths of 609 (95% CI: 415-775) to 2,860 (95% CI: 1,436-4,273) in the short term, depending on the relative risk chosen from the epidemiological studies. Interpretation: Our study indicates that lockdown measures in China led to abrupt reductions in ambient PM2.5 concentration but also led to significant increases in indoor PM2.5 exposure due to confined indoor activities and increased usages of household fuel for cooking and heating. We estimated that hundreds of premature deaths were added as a combination of decreased ambient PM2.5 and increased hou."
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 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [2];
Q3 = [];
Q4 = [12];
Q5 = [1];
Q6 = [1].
Non-negligible contributions to human health from increased household air pollution exposure during the COVID-19 lockdown in China
Background: Ambient and household air pollution are found to lead to premature deaths from all-cause or cause-specific death. The national lockdown measures in China during COVID-19 were found to lead to abrupt changes in ambient surface air quality, but indoor air quality changes were neglected. In this study, we aim to investigate the impacts of lockdown measures on both ambient and household air pollution as well as the short-term health effects of air pollution changes. Methods: In this study, an up-to-date emission inventory from January to March 2020 in China was developed based on air quality observations in combination with emission-concentration response functions derived from chemical transport modeling. These emission inventories, together with the emissions data from 2017 to 2019, were fed into the state-of-the-art regional chemistry transport model to simulate the air quality in the North China Plain. A hypothetical scenario assuming no lockdown effects in 2020 was also performed to determine the effects of the lockdown on air quality in 2020. A difference-to-difference approach was adopted to isolate the effects on air quality due to meteorological conditions and long-term decreasing emission trends by comparing the PM2.5 changes during lockdown to those before lockdown in 2020 and in previous years (2017-2019). The short-term premature mortality changes from both ambient and household PM2.5 changes were quantified based on two recent epidemiological studies, with uncertainty of urban and rural population migration considerations. Findings: The national lockdown measures during COVID-19 led to a reduction of 5.1 mu g m(-3) in ambient PM2.5 across the North China Plain (NCP) from January 25th to March 5th compared with the hypothetical simulation with no lockdown measures. However, a difference-to-difference method showed that the daily domain average PM2.5 in the NCP decreased by 9.7 mu g m(-3) between lockdown periods before lockdown in 2020, while it decreased by 7.9 mu g m(-3) during the same periods for the previous three-year average from 2017 to 2019, demonstrating that lockdown measures may only have caused a 1.8 mu g m(-3) decrease in the NCP. We then found that the integrated population-weighted PM2.5, including both ambient and indoor PM2.5 exposure, increased by 5.1 mu g m(-3) during the lockdown periods compared to the hypothetical scenario, leading to additional premature deaths of 609 (95% CI: 415-775) to 2,860 (95% CI: 1,436-4,273) in the short term, depending on the relative risk chosen from the epidemiological studies. Interpretation: Our study indicates that lockdown measures in China led to abrupt reductions in ambient PM2.5 concentration but also led to significant increases in indoor PM2.5 exposure due to confined indoor activities and increased usages of household fuel for cooking and heating. We estimated that hundreds of premature deaths were added as a combination of decreased ambient PM2.5 and increased household PM2.5. Our findings suggest that the reduction in ambient PM2.5 was negated by increased exposure to household air pollution, resulting in an overall increase in integrated population weighted exposure. Although lockdown measures were instrumental in reducing the exposure to pollution concentration in cities, rural areas bore the brunt, mainly due to the use of dirty solid fuels, increased population density due to the large-scale migration of people from urban to rural areas during the Chinese New Year and long exposure time to HAP due to restrictions in outdoor movement.