ENEA, CR Casaccia, Via Anguillarese, Rome, Italy.
CIEMAT, Avda. Complutense, 40, Madrid, Spain.
Environ Res. 2022 Aug;211:113048. doi: 10.1016/j.envres.2022.113048. Epub 2022 Mar 4.
Tropospheric ozone (O) is one of the most concernedair pollutants dueto its widespread impacts on land vegetated ecosystems and human health. Ozone is also the third greenhouse gas for radiative forcing. Consequently, it should be carefully and continuously monitored to estimate its potential adverse impacts especially inthose regions where concentrations are high. Continuous large-scale O concentrations measurement is crucial but may be unfeasible because of economic and practical limitations; therefore, quantifying the real impact of Oover large areas is currently an open challenge. Thus, one of the final objectives of O modelling is to reproduce maps of continuous concentrations (both spatially and temporally) and risk assessment for human and ecosystem health. We here reviewedthe most relevant approaches used for O modelling and mapping starting from the simplest geo-statistical approaches andincreasing in complexity up to simulations embedded into the global/regional circulation models and pro and cons of each mode are highlighted. The analysis showed that a simpler approach (mostly statistical models) is suitable for mappingOconcentrationsat the local scale, where enough Oconcentration data are available. The associated error in mapping can be reduced by using more complex methodologies, based on co-variables. The models available at the regional or global level are used depending on the needed resolution and the domain where they are applied to. Increasing the resolution corresponds to an increase in the prediction but only up to a certain limit. However, with any approach, the ensemble models should be preferred.
对流层臭氧(O)是最受关注的空气污染物之一,因为它广泛影响陆地植被生态系统和人类健康。臭氧也是第三大辐射强迫温室气体。因此,应仔细并持续监测臭氧,以评估其潜在的不利影响,尤其是在浓度较高的地区。尽管连续大规模测量臭氧浓度至关重要,但由于经济和实际限制,这可能并不可行;因此,目前量化臭氧对大面积地区的实际影响是一个开放性挑战。因此,臭氧建模的最终目标之一是再现连续浓度(时空)图以及对人类和生态系统健康的风险评估。我们在这里回顾了从最简单的地统计学方法开始并逐渐增加到嵌入全球/区域环流模型中的模拟的最相关的臭氧建模和制图方法,并强调了每种方法的优缺点。分析表明,对于局部地区臭氧浓度的制图,更简单的方法(主要是统计模型)是合适的,因为那里有足够的臭氧浓度数据。通过使用基于协变量的更复杂方法,可以减少映射中的相关误差。区域或全球层面的模型根据所需的分辨率和应用域来使用。提高分辨率对应于预测的增加,但仅增加到一定的限制。然而,无论采用哪种方法,都应优先采用集合模型。