Estimating the Causal Effects of Cruise Traffic on Air Pollution using Randomization-Based Inference

Local environmental organizations and media have recently expressed concerns over air pollution induced by maritime traffic and its potential adverse health effects on the population of Mediterranean port cities. We explore this issue with unique high-frequency data from Marseille, France's largest port for cruise ships, over the 2008-2018 period. Using a new pair-matching algorithm designed for time series data, we create hypothetical randomized experiments and estimate the variation in air pollutant concentrations caused by a short-term increase in cruise vessel traffic. We carry out a randomization-based approach to compute 95% Fisherian intervals (FI) for constant treatment effects consistent with the matched data and the hypothetical intervention. At the hourly level, cruise vessels' arrivals increase concentrations of nitrogen dioxide (NO$_{2}$) by 4.7 $\mu g/m^3$ (95% FI: [1.4, 8.0]), of sulfur dioxide (SO$_{2}$) by 1.2 $\mu g/m^3$ (95% FI: [-0.1, 2.5]), and of particulate matter (PM$_{10}$) by 4.6 $\mu g/m^3$ (95% FI: [0.9, 8.3]). At the daily level, cruise traffic increases concentrations of NO$_{2}$ by 1.2 $\mu g/m^3$ (95% FI: [-0.5, 3.0]) and of PM$_{10}$ by 1.3 $\mu g/m^3$ (95% FI: [-0.3, 3.0]). Our results suggest that well-designed hypothetical randomized experiments provide a principled approach to better understand the negative externalities of maritime traffic.