This study examines the statistical relationship between people's movements and difference in air quality between the origin and the destination, using computational social science approach. We put forward an important environmentally driven human movement question: Would people move to places of better air qualities? Utilizing Twitter Streaming API, 39,928,132 randomized geo-coded tweets posted by 8,551,557 individual users during the period of 01/01/2016 – 31/12/2017 were extracted to examine the movements of Twitter users. Coupling Twitter users’ international movements with air quality data, our results have shown that Twitter users tend to move to places of better air qualities in general. We find that in most countries, the net move-out rate tends to be positively correlated with the monthly PM_2.5 concentration in the origin country, and the number of international movements to the destination country is negatively correlated with the decrease in PM_2.5 concentration in the origin country. For domestic movements, our results have shown that Twitter users in Indian cities, including Mumbai, one of the largest and highly polluted cities, tend to move to other Indian cities of better air qualities. Our results confirm that people across different parts of the world tend to move to places of better air qualities.

这项研究使用计算社会科学方法研究了人的运动与始发地与目的地之间空气质量差异之间的统计关系。我们提出了一个重要的环境驱动的人类运动问题：人们会搬到空气质量更好的地方吗？利用Twitter Streaming API，提取了2016年1月1日至2017年12月31日期间8,551,557个人用户发布的39,928,132个随机地理编码推文，以检查Twitter用户的活动。将Twitter用户的国际活动与空气质量数据结合起来，我们的结果表明，Twitter用户通常倾向于空气质量较好的地方。我们发现，在大多数国家/地区，净迁移率与月度PM _2.5呈正相关。集中在原籍国，国际转移到目的地国的次数与原籍国PM _2.5浓度的下降呈负相关。对于国内运动，我们的结果表明，印度城市的Twitter用户（包括孟买）是印度最大和污染最严重的城市之一，他们倾向于使用其他空气质量更好的印度城市。我们的结果证实，世界各地的人们倾向于搬到空气质量更好的地方。