Current state of the global operational aerosol multi-model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)

Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches

Identification and mapping of larval sources are a prerequisite for effective planning and implementing mosquito larval source management (LSM). Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats by using multimodel ensemble modeling, which includes selection of models, ensembling method, and predictors, evaluation of variable importance, prediction of potential larval habitats, and assessment of prediction uncertainty. The models were built and validated based on multisite, multiyear field observations and climatic/environmental variables. Model performance was tested using independent field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors in western Kenya were elevation, geomorphon class, and precipitation for the 2 months prior. Additional predictors may be required to increase the predictive accuracy of the larva-positive habitats. This is the first study to provide a detailed framework for the process of multimodel ensemble modeling for malaria vector habitats. Mapping of potential habitats will be helpful in LSM planning.

Analysis of nighttime aerosols and relation to covariates over a highly polluted sub-Saharan site using Mann-Kendall and wavelet coherence approach

High emissions of aerosols and trace gases during nighttime can cause serious air quality, climate, and health issues, particularly in extremely polluted cities. In this paper, an effort has been made to examine the variations in aerosols and trace gases over a sub-Saharan city of Ilorin (Nigeria) during nighttime. We have used Aerosol Robotic Network data of aerosol optical depth (AOD) at 500 nm, Angstrom exponent (AE) (440/870), and precipitable water (WVC). Both AE and WVC showed a decreasing trend of -0.0012% and -0.0010% per year, respectively. We also analyzed nighttime data of carbon monoxide (CO), methane (CH4 ), and ozone (O3 ) from Atmospheric Infrared Sounder and aerosol subtypes from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation). AOD, AE, and WVC average values are found to be 0.64 ± 0.33, 0.74 ± 0.24, and 3.40 ± 0.97, respectively. As a result of northeasterly winds carrying Saharan dust during the dry season, the greatest value of AOD (1.29) was observed in February. Desert dust aerosols (37.63%) were the most prevalent type, followed by mixed aerosols (44.15%). Winds at a height of 1500 m above ground level were likely transporting Saharan dust to Ilorin. CALIPSO images revealed that Ilorin's atmosphere contained dust, polluted continental, clean maritime, and polluted dust on high AOD days. The National Oceanic and Atmospheric Administration's vertical sounding profiles showed that the presence of high AOD values was caused by the inversion layer trapping aerosol pollution. Average nighttime concentrations of CO, O3 , and CH4 were measured to be 127 ± 18, 29.7 ± 2.1, and 1822.6 ± 12.7 ppbv, respectively. The wavelet coherence spectra exhibited significant quasi-biannual and quasi-annual oscillations at statistically significant levels.

Assessing ozone abatement scenarios in the framework of the Spanish ozone mitigation plan

Tropospheric ozone (O3) is a secondary air pollutant that affects human health, vegetation and climate, especially in Mediterranean countries such as Spain. In order to tackle this long-standing issue, the Spanish government recently started to design the Spanish O3 Mitigation Plan. To support this initiative and ultimately provide recommendations, we performed a first ambitious emission and air quality modeling exercise. This study presents the development of different emission scenarios - aligned with or beyond the measures planned for 2030 in Spain - and the modeling of their respective impact on the O3 pollution across Spain (in July 2019) with both MONARCH and WRF-CMAQ air quality models. The modeling experiments include a base case scenario, a so-called planned emission (PE) scenario integrating the expected emission changes related to 2030, and a set of specific emission scenarios in which additional emission changes are applied to specific sectors (on e.g., road transport, maritime traffic) on top of the PE scenario. The planned emission scenario considerably reduces daily 8-h maximum O3 concentrations (-4 μg/m3 on average), with strongest reductions in Madrid region, north of Catalonia, Valencia region, Galicia and Andalusia. The frequency of observed daily exceedances of the 120 μg/m3 daily 8-h maximum target value and 180 μg/m3 hourly information threshold could be reduced by -37 and -77 %, respectively. The results of the specific scenarios highlight road transport and maritime traffic as two key emission sectors contributing to O3 pollution, over the entire country and the Mediterranean coast, respectively, while solvent use and industry emissions have a more limited and localized impact on O3. In any case, even with the implementation of all the emission scenarios, daily exceedances of the aforementioned thresholds will still be recorded over the country.

Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields

We consider several approaches to a design of a regional-to-continent-scale automatic pollen monitoring network in Europe. Practical challenges related to the arrangement of such a network limit the range of possible solutions. A hierarchical network is discussed, highlighting the necessity of a few reference sites that follow an extended observations protocol and have corresponding capabilities. Several theoretically rigorous approaches to a network design have been developed so far. However, before starting the process, a network purpose, a criterion of its performance, and a concept of the data usage should be formalized. For atmospheric composition monitoring, developments follow one of the two concepts: a network for direct representation of concentration fields and a network for model-based data assimilation, inverse problem solution, and forecasting. The current paper demonstrates the first approach, whereas the inverse problems are considered in a follow-up paper. We discuss the approaches for the network design from theoretical and practical standpoints, formulate criteria for the network optimality, and consider practical constraints for an automatic pollen network. An application of the methodology is demonstrated for a prominent example of Germany's pollen monitoring network. The multi-step method includes (i) the network representativeness and (ii) redundancy evaluation followed by (iii) fidelity evaluation and improvement using synthetic data.

Bioaerosols in the atmosphere at two sites in Northern Europe in spring 2021: Outline of an experimental campaign

A coordinated observational and modelling campaign targeting biogenic aerosols in the air was performed during spring 2021 at two locations in Northern Europe: Helsinki (Finland) and Siauliai (Lithuania), approximately 500 km from each other in north-south direction. The campaign started on March 1, 2021 in Siauliai (12 March in Helsinki) and continued till mid-May in Siauliai (end of May in Helsinki), thus recording the transition of the atmospheric biogenic aerosols profile from winter to summer. The observations included a variety of samplers working on different principles. The core of the program was based on 2- and 2.4--hourly sampling in Helsinki and Siauliai, respectively, with sticky slides (Hirst 24-h trap in Helsinki, Rapid-E slides in Siauliai). The slides were subsequently processed extracting the DNA from the collected aerosols, which was further sequenced using the 3-rd generation sequencing technology. The core sampling was accompanied with daily and daytime sampling using standard filter collectors. The hourly aerosol concentrations at the Helsinki monitoring site were obtained with a Poleno flow cytometer, which could recognize some of the aerosol types. The sampling campaign was supported by numerical modelling. For every sample, SILAM model was applied to calculate its footprint and to predict anthropogenic and natural aerosol concentrations, at both observation sites. The first results confirmed the feasibility of the DNA collection by the applied techniques: all but one delivered sufficient amount of DNA for the following analysis, in over 40% of the cases sufficient for direct DNA sequencing without the PCR step. A substantial variability of the DNA yield has been noticed, generally not following the diurnal variations of the total-aerosol concentrations, which themselves showed variability not related to daytime. An expected upward trend of the biological material amount towards summer was observed but the day-to-day variability was large. The campaign DNA analysis produced the first high-resolution dataset of bioaerosol composition in the North-European spring. It also highlighted the deficiency of generic DNA databases in applications to atmospheric biota: about 40% of samples were not identified with standard bioinformatic methods.

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD550. Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s−1, the C4C and MODIS AOD550s also overestimate CAOD550 and FAOD550, respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely even larger than what was diagnosed here. We conclude with a discussion on how MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research.

Improving surface PM2.5 forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi-satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multi chemical transport model (CTM) (GEOS-Chem, WRF-Chem and CMAQ) outputs and ground observations. In part I of the study, a multi-model ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non-rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125 - 300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non-rural areas, referred to as "extended ground truth" or EGT, for today. Lastly, we applied the KF technique to update the bias in the forecast for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three models for both today and next day show the best performance. Together, the two-part study develops a multi-model and multi-AOD bias correction technique that has the potential to improve PM2.5 forecasts in both rural and non-rural areas in near real time, and be readily implemented at state levels.

Aerosol Microbiome over the Mediterranean Sea Diversity and Abundance

Prokaryotic microbes can become aerosolized and deposited into new environments located thousands of kilometers away from their place of origin. The Mediterranean Sea is an oligotrophic to ultra-oligotrophic marginal sea, which neighbors northern Africa (a major source of natural aerosols) and Europe (a source of mostly anthropogenic aerosols). Previous studies demonstrated that airborne bacteria deposited during dust events over the Mediterranean Sea may significantly alter the ecology and function of the surface seawater layer, yet little is known about their abundance and diversity during ‘background’ non-storm conditions. Here, we describe the abundance and genetic diversity of airborne bacteria in 16 air samples collected over an East-West transect of the entire Mediterranean Sea during non-storm conditions in April 2011. The results show that airborne bacteria represent diverse groups with the most abundant bacteria from the Firmicutes (Bacilli and Clostridia) and Proteobacteria (Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria) phyla. Most of the bacteria in our samples have previously been observed in the air at other open ocean locations, in the air over the Mediterranean Sea during dust storms, and in the Mediterranean seawater. Airborne bacterial abundance ranged from 0.7 × 104 to 2.5 × 104 cells m−3 air, similar to abundances at other oceanic regimes. Our results demonstrate that airborne bacterial diversity is positively correlated with the mineral dust content in the aerosols and was spatially separated between major basins of the Mediterranean Sea. To our knowledge, this is the first comprehensive biogeographical dataset to assess the diversity and abundance of airborne microbes over the Mediterranean Sea. Our results shed light on the spatiotemporal distribution of airborne microbes and may have implications for dispersal and distribution of microbes (biogeography) in the ocean.