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The small-scale variability of fog over Paris-CdG airport was explored by using the twelve visibility measurements installed on runways. The variability inside this sub-kilometre area is significant for fog, whatever the studied fog characteristics. These results confirm that about 50% of fog is thin and heterogeneous at the airport scale. This study indicated that one point of measurement may be far from the representativeness of sub-kilometre scale area and can create problems when used for verifying numerical models. The fog characteristics cannot be captured by a local measurement of visibility, and it is not possible to deduce fog extension at the local scale from a local measurement, even for low visibility such as for LVP cases (Low Visibility Procedures). This work demonstrated with a perfect forecast framework that a large dispersion of forecast scores could be obtained when using one observation at the local-scale due to representativeness errors of visibility measurements. This dispersion has the same order of magnitude as the current NWP forecast quality of fog. An attempt to quantify the scale heterogeneity of fog was made using the Gini index. This index has allowed highlighting the appearance of waves during the dissipation phase of fog.

In the last few decades, urban planning has expanded regarding environmental considerations. However, air quality, which is regarded as an important aspect of the green development of cities, is not considered in urban planning. This research aims to propose a tool to easily introduce air quality considerations into urban projects. Nowadays, the usual air pollutants (NOx, PM, SO2, and O3) are measured via sophisticated monitoring stations—or even low-cost devices—to give near-real-time air quality indices. However, stations are not adapted to local air pollution and real-time data are not helpful for planning purposes. An index able to rank areas and projects based on urban “air proxy data” would help decision makers. This paper presents how to create an air quality index as a decision support tool for urban planning. No pollutant measurement campaign will be necessary and only data that are easily accessible, even to nonexperts, are used. This paper describes the methodological development of an index that we call AQOI (Air Quality Observed Index), and the results obtained for four different locations (industrial, urban, and rural) considered as preliminary tests.

The concentration of surface ozone (O3) strongly depends on environmental and meteorological variables through a series of complex and non-linear functions. This study aims to explore the performances of an advanced machine learning (ML) method, the boosted regression trees (BRT) technique, in exploring the relationships between surface O3 and its driving factors, and in predicting the levels of O3 concentrations. To this end, a BRT model was trained on hourly data of air pollutants and meteorological parameters, acquired, over the 2016–2018 period, in a rural area affected by an anthropic source of air pollutants. The abilities of the BRT model in ranking, visualizing, and predicting the relationship between ground-level O3 concentrations and its driving factors were analyzed and illustrated. A comparison with a multiple linear regression (MLR) model was performed based on several statistical indicators. The results obtained indicated that the BRT model was able to account for 81% of changes in O3 concentrations, it slightly outperforms the MLR model in terms of the predictions accuracy and allows a better identification of the main factors influencing O3 variability on a local scale. This knowledge is expected to be useful in defining effective measures to prevent and/or mitigate the health damages associated with O3 exposure.

Based on the Koppen Geiger (KG) classification system, this review article examines existing studies and projects that have endeavoured to address local outdoor thermal comfort thresholds through Public Space Design (PSD). The review is divided into two sequential stages, whereby (1) overall existing approaches to pedestrian thermal comfort thresholds are reviewed within both quantitative and qualitative spectrums; and (2) the different techniques and measures are reviewed and framed into four Measure Review Frameworks (MRFs), in which each type of PSD measure is presented alongside its respective local scale urban specificities/conditions and their resulting thermal attenuation outcomes. The result of this review article is the assessment of how current practices of PSD within three specific subcategories of the KG ‘Temperate’ group have addressed microclimatic aggravations such as elevated urban temperatures and Urban Heat Island (UHI) effects. Based upon a bottom-up approach, the interdisciplinary practice of PSD is hence approached as a means to address existing and future thermal risk factors within the urban public realm in an era of potential climate change.

This study examined the role of meteorological processes in two of the highest ozone (O3) episodes within the last decade at monitoring sites in southern New Hampshire (NH), USA. The highest O3 levels occurred on 14 August 2002 at Thompson Farm (TF) and 22 July 2004 at Castle Springs (CS). Ozone mixing ratios in the 2002 episode showed continual high values (>100 ppbv) at the beginning of the episode, and reached 151 ppbv on 14 August. The 2004 episode consisted of one day of high O3 (>100 ppbv) on 22 July at CS with the peak level of 111 ppbv. Our analysis suggested that the August 2002 high O3 event at TF occurred under stagnant synoptic high-pressure conditions that prevailed over the entire eastern USA for an unusually extended time period. The clear skies and stable meteorological conditions resulted in accumulation of pollutants in the boundary layer. At the same time, the mesoscale low-level-jet (LLJ) played an important role in transporting air masses from the polluted Mid-Atlantic areas to the Northeast. Local land-sea-breeze circulations also added to the impact of this event. Our examination showed that the unprecedented high levels of O3 on 22 July 2004 at CS was driven by two mechanisms, stratospheric intrusion and the Appalachian lee trough (APLT), which was not found during other O3 episodes at the site in the decade long data record. This study demonstrated that unusually high O3 levels at New England rural sites were driven by multi-scale processes, and the regional/local scale processes controlled the magnitude and timing of the local pollution episodes.

In regions of complex topography, local flows are difficult to forecast on a routine basis, especially in stable conditions, due to the coarse resolution of operational models. The Cadarache valley (southeastern France) features this sort of complex topography. The Weather Research and Forecasting (WRF) model is run daily to forecast the weather in this region with a horizontal resolution of 3 km. Such a resolution cannot resolve all topography details of the small Cadarache valley, and therefore its local wind patterns. Other variables, however, that are less dependent on the subgrid topography, are satisfactorily forecasted, and used as inputs to an artificial neural network (ANN) designed to reproduce wind observations inside the valley from WRF forecasts. A variable selection procedure identified 5 key input variables that best drive the ANN. With respect to the WRF output, the ANN significantly improves forecasted low-level winds, both for speed and direction. This study demonstrates the potential for the ANN technique to be used as a correcting tool to forecast weather conditions at the local scale when numerical modeling is performed at a resolution too coarse to take into account the effect of local topography.