预测加拿大蒙特利尔和多伦多一年平均室外超细颗粒度的空间变化:整合土地利用回归和深度学习模型
Marshall Lloyd1, Arman Ganji2, Junshi Xu2
1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec H3A 1G1, Canada.
Environment international
|August 6, 2023
概括
新的模型估计了城市内的户外超细颗粒 (UFP) 和黑碳 (BC) 污染变化. 这些模型结合了土地利用和卫星数据,为健康研究提供了关键数据.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 城市规划 城市规划
背景情况:
- 户外超细颗粒 (UFP) 和黑碳 (BC) 度在城市环境中表现出显著的空间变化.
- 长期暴露于UFPs和BC与各种不良健康结果有关,需要准确的暴露评估.
研究的目的:
- 开发和验证新型模型,用于估计城市内每年室外UFP和BC度中位数的空间变化.
- 还要估计加拿大最大的城市蒙特利尔和多伦多的UFP规模.
主要方法:
- 综合移动监测活动,使用土地使用参数的通用添加模型 (GAM) 和使用卫星图像的深度卷积神经网络 (CNN) 模型.
- 通过整合来自GAM和CNN的预测来开发组合模型,以提高空间估计.
主要成果:
- 与GAM相比,组合模型显示空间变异解释略有改善 (R 2) 和显着比单独的CNN模型更好的性能.
- 在多伦多,组合模型R2值达到了UFP度的0.73,UFP大小的0.55,BC度的0.61.
- 在蒙特利尔,组合模型R2值为UFP度为0.60,UFP大小为0.49,BC度为0.60.
结论:
- 开发的模型准确估计了城市内UFP和BC度和UFP大小的空间变化.
- 这些模型的预测很容易获得,以促进未来关于空气污染对健康的影响的流行病学研究.
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