Air pollution declines during COVID-19 lockdowns mitigate the global health burden.

The lockdown response to COVID-19 has resulted in an unprecedented reduction in global economic activity and associated air pollutant levels, especially from a decline in land transportation. We utilized a network of >10,000 air quality stations distributed over 34 countries during lockdown dates up until 15 May 2020 to obtain lockdown related anomalies for nitrogen dioxide, ozone and particulate matter smaller than 2.5 μm in diameter (PM).

COVID-19 lockdowns cause global air pollution declines.

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO: 60% with 95% CI 48 to 72%), and fine particulate matter (PM: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May.

Ambient Air Pollution Increases the Risk of Cerebrovascular and Neuropsychiatric Disorders through Induction of Inflammation and Oxidative Stress.

Exposure to ambient air pollution is a well-established determinant of health and disease. The Lancet Commission on pollution and health concludes that air pollution is the leading environmental cause of global disease and premature death. Indeed, there is a growing body of evidence that links air pollution not only to adverse cardiorespiratory effects but also to increased risk of cerebrovascular and neuropsychiatric disorders.

Loss of life expectancy from air pollution compared to other risk factors: a worldwide perspective.

Aims: Long-term exposure of humans to air pollution enhances the risk of cardiovascular and respiratory diseases. A novel Global Exposure Mortality Model (GEMM) has been derived from many cohort studies, providing much-improved coverage of the exposure to fine particulate matter (PM2.5).

sPop: Age-structured discrete-time population dynamics model in C, Python, and R.

This article describes the sPop packages implementing the deterministic and stochastic versions of an age-structured discrete-time population dynamics model. The packages enable mechanistic modelling of a population by monitoring the age and development stage of each individual. Survival and development are included as the main effectors and they progress at a user-defined pace: follow a fixed rate, delay for a given time, or progress at an age-dependent manner.