Session Presentations

Waluyo Eko Cahyono, Wiwiek Setyawati, Retno Puji Lestari, Asri Indrawati and Dyah Aries Tanti

Acid deposition represents the mix of air pollutants that deposit from the atmosphere. Acid deposition emerged as the result of the impact of air pollution due to the increased emissions of SOx and NOx. This study aims to identify the state of acid deposition through rainwater monitoring in selected sites. The two components of acidic deposition are dry deposition and wet deposition. Acid deposition samples in Indonesia were collected using rainwater sampler in several sites during the period of 2009-2018, namely Serpong, Bandung, Jakarta and Kototabang . In each site, measurement of rainwater precipitation, pH, electro conductivity, and analysis of ions were performed. The characteristic of wet deposition pollutant in Serpong showed the NH4+ and NO3- ions are the most dominant, while in Jakarta and Bandung showed the influence of NH4+ and SO42-. H+ and Cl- ions were the prominent feature found in remote areas of Kototabang as the representative of rural area. Monitoring of dry deposition in sub urban site is considthered as a part of acid deposition observation, in which can be used for the assessment of acid deposition impact to environment. Dry deposition measurement was conducted using filter pack method to determine the particulate component of Na+, K+, Ca2+, Cl-, NO3-, and SO42- in aerosol and gases of SO2, HNO3, NH3, and HCl. The air is inhaled using a pump with a flow rate of 1 L/min for 14 days continuously, and then passed into a four stage filter pack, whereas in each filter set is specifically absorbed each chemical component. Pollutant sources from agricultural and farm sectors nearby the sampling sites potentially lead to high level of NH3 in gas phase, while anthropogenic activities contributed to increase the SO42- concentration in aerosol phase.

Fernando Campo, Andy Blanco-Rodriquez, Robson Will, Thiago Vieira Vasques, Nathan Campos Teixeira, Davide Franco, Alejandro Garcia-Ramirez, Leonardo Hoinaski

In this work, we present the preliminary results of deploying mobile and fixed air sensor platforms in the city of Florianopolis, Brazil, as a way of addressing the matter of collecting locally representative air quality data. The basic structure of the sensor nodes consists of: an Arduino Mega, an array of electrochemical gas sensors, a GPS module for geo-location, an SD card for data storage, a Real-Time Clock, and an ESP8266 module for Wi-Fi communication. Each node measures gaseous pollutants that are regulated by the Brazilian environmental laws. Both mobile and fixed nodes use digital gas sensors for IoT from SPEC Sensors; the fixed node, in particular, includes an array of Alphasense B4 series sensors for air quality networks.

The problem of local representativeness of air quality data is an issue for government offices and environmental agencies. The lack of relevant information may result in a deficient estimation of air pollution and on the implementation of poorly effective air quality management policies. This topic is especially relevant in Brazil, where regulatory monitoring is still limited. The high costs of the regulatory stations and their maintenance, as well as the lack of qualified staff, hamper the deployment of spatially broader and denser monitoring networks.

The main objective of this presentation is to discuss the performance of the sensor platforms according to some expected behavior and the influence of environmental conditions. The way to tackle this is by checking the existence of a correspondence between the sensor responses, the daily and weekly traffic patterns, and the levels of air pollution expected at different locations. Another analysis compares the outputs of Alphasense versus SPEC sensors, as well as the mobile node versus a Sniffer 4D V1 multi-gas detector. Finally, the effect of environmental variables such as temperature and relative humidity on the electrochemical sensors is analyzed.

The air sensors systems developed are still in its prototype phase and require further enhancements and assessment, especially what regards to laboratory calibrations, in-field co-location with regulatory monitoring stations, and long term performance. However, the results that this work presents point to more basic problems on the platforms that require improvement and also define a pathway for future analysis and processing of new data. These platforms have the potential for increasing the spatial and temporal resolution of monitoring networks in the city, as well as for opening the way to new monitoring services and applications such as the creation of air pollution maps, the detection of "hotspots" in the city, citizen science and education on air pollution topics, assessment of personal exposure to gas contaminants and evaluation of the impacts that the performance of physical activities on polluted environments has to the human health.

Peiyang Cheng, Arastoo Pour-Biazar, Andrew T. White

An accurate estimate of insolation is critical for air quality simulations since the production rates of many criteria air pollutants can be greatly affected by the strength of solar radiation. This study implemented the Geostationary Operational Environmental Satellite (GOES)-based cloud assimilation technique developed by White et al. in a WRF-SMOKE-CMAQ modeling framework to improve the estimation of photolysis rates and criteria gases. The simulation was carried out on a 12-km domain to study the 2016 ozone season over the contiguous United States (CONUS). Results from this study will be presented.

Jingting Huang ^[1,2], W. Patrick Arnott ^[2], James C. Barnard ^[2], Heather A. Holmes ^[1,2]

[1] Department of Chemical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.

[2] Atmospheric Sciences Program, Department of Physics, University of Nevada-Reno, Reno, NV, 89557, USA.

Determining the surface air pollution concentrations is a key component to investigating the human and environmental impacts of air pollution globally. Surface concentrations from monitors are not available in many locations, therefore data from satellite remote sensing or chemical transport models have been increasingly used to estimate surface pollutant concentrations worldwide. While this increases the spatial coverage of the pollution estimates there are still uncertainties in these methods that require investigation. This presentation will focus on uncertainties in satellite remote sensing products that estimate aerosol optical depth (AOD).

Increased efforts are needed to minimize uncertainties in satellite aerosol retrieval algorithms that determine AOD in response to a knowledge gap associated with representative parameterized aerosol properties and surface reflectance characterization. The overarching goal of this study is to recognize the underlying physical processes that contribute to low-quality satellite AOD retrievals. We outline a conceptual 1-D radiative transfer model (RTM) together with a traditional propagation of error approach to estimate uncertainties in satellite AOD products using analytical equations. This simplified form of the RTM helps to identify critical surface albedo (CSA), critical single scattering albedo (CSSA), and critical asymmetry parameter (CAP) for use in uncertainty analysis of satellite AOD; at a critical value, the top-of-atmosphere (TOA) reflectance is insensitive to significant variability in AOD. Even a slight low bias (~1%) in the single scattering albedo (SSA), asymmetry parameter (g) or surface albedo (A), especially when prescribed aerosol optical properties and underlying surfaces with albedo are close to their critical values, will lead to significant errors (>100%) in satellite AOD retrievals. While the 1-D RTM used in this study can only describe limited aspects of uncertainties that arise in the satellite remote sensing of aerosols, the results presented are expected to physically and mathematically provide a preliminary framework for the application of the critical value method on the upcoming refinement of satellite aerosol retrieval algorithms.

Farzaneh Taksibi, Hossein Khajehpour, Yadollah Saboohi

Policymakers use emission rates of different sources to determine the causes of air pollutants and to target controlling actions. Emissions inventories are an essential input to dispersion models that estimate air quality. Potential regulatory actions can be predicted by applying estimated emissions reductions to emissions inventory data in air quality models. Methods to determine the relative significance of emissions sources include emission inventories, source apportionments, and sensitivity analyses of emissions. The choice of the proper method depends on the availability of data, time, expertise, and cost. The VIIRS Nightfire products can detect thermal anomalies to study the spatial and temporal changes built on the well-established MODIS Fire and Thermal Anomalies product. In this method, unaccounted emission sources that are unknown to the policymakers will be identified. One of the main unidentified sources is the crop residue burning and agricultural wastes. This study provides a remote sensing methodology for identifying the time and place of the thermal anomalies around Tehran with the help of VIIRS Nightfire products, which offers a quantitative estimation of the temperatures of sub-pixel heat sources. This is used to evaluate the spatiotemporal distribution of uncontrolled burning and its impact on Tehran's air quality. In this case, thermal anomalies around Tehran are investigated from 2017 to 2019, and their impact on PM concentrations at nearby air quality monitoring stations is analyzed. Based on topographic and meteorological conditions, and with the Hysplit model's help, the effect of agricultural waste burning on Tehran is evaluated. Finally, the nonsystematic emission sources' effectiveness on Tehran's air quality through sensitivity analysis of the observed concentration is analyzed.

Maria Makarova¹, Frank Hase², Dmitry Ionov¹, Carlos Alberti², Stefani Foka¹, Thomas Blumenstock², Vladimir Kostsov¹, Thorsten Warneke³, Yana Virolainen¹, Anatoly Poberovskii¹

¹ Department of Atmospheric Physics, Faculty of Physics, St. Petersburg State University, Russia

² Institute of Meteorology and Climate Research IMK-ASF, Karlsruhe Institute of Technology, Karlsruhe, Germany

³ University of Bremen, Germany

In 2019 and 2020, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was conducted within the St. Petersburg agglomeration (Russia) aiming to estimate the emission intensity of greenhouse (CO₂, CH₄) and reactive (CO, NO_x) gases for St. Petersburg which is the largest Northern megacity. The area of St. Petersburg urban agglomeration is  about 1440 km². As of 2018, the city has a population of ~5.4 million people (the official data for 2018); according to unofficial data the population is now more than 7 million. The entire campaign consisted of 11 mostly cloudless days of measurements in March-April 2019 and 6 days in March-early May 2020. In 2020, the EMME experiment was carried out during three days before the COVID 19 pandemic lockdown and three days during the lockdown.
The core instruments of the campaign were two portable FTIR spectrometers Bruker EM27/SUN which were used for ground-based remote sensing measurements of the total column amount of CO₂, CH₄ and CO at upwind and downwind locations on the opposite sides of the city. The NO₂ tropospheric column amount was observed along a circular highway around the city by continuous mobile measurements of scattered solar visible radiation with OceanOptics HR4000 spectrometer using the DOAS technique.

The estimates of the St. Petersburg area fluxes for the considered greenhouse and reactive gases were obtained by coupling a box model and the results of the EMME observational campaign using the mass balance approach. The area fluxes for St. Petersburg city center were estimated as (89 x 28) kt km^-2 yr^-1 of CO2, (135 x 68) t km^-2 yr^-1 of CH₄, (251 - 104) t km^-2 yr^-1 of CO and (66 - 28) t km^-2 y^r-1 of NO_x.

Coupling the EMME 2019&2020 observations with HYSPLIT simulations and the a priori information on CO₂ emissions from the ODIAC database, we evaluated the total CO₂ emission from St.Petersburg megacity which amounts to (76-5) million tons per year for 2019 and (68-7) million tons per year for 2020.

Acknowledgements

This activity has received funding from the European Union's Horizon 2020 research and innovation programme under grantagreement No 776810 (VERIFY project). The research was supported by Russian Foundation for Basic Research through the project No.18-05-00011. Ancillary experimental data were acquired using the scientific equipment of "Geomodel" research centre of St. Petersburg State University.

Junhua Zhang, Michael D. Moran, Paul A. Makar, and Shailesh Kharol

Leaf Area Index (LAI), which is defined as the amount of leaf area per unit surface of the ground (m²/m²), is used in air quality models for land surface process parameterizations and for calculating biogenic emissions. The MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product provided by NASA (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php) has been widely used by the air quality modeling community for such purposes, including the estimation of biogenic emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature, https://sites.google.com/uci.edu/bai/megan/data-and-code/lai). However, some limitations of the MODIS LAI product have been identified, such as the unreasonably low LAI estimated for evergreen needleleaf boreal forests in the Northern Hemisphere during wintertime due to snow cover and low sun angle (e.g., Fig. 4 of Messina et al., 2016, https://doi.org/10.5194/acp-16-14169-2016) and gaps in coverage due to missing retrievals in regions with persistent cloud cover. Considerable effort has been devoted to improving the MODIS LAI product (e.g., Yuan et al., 2011, https://doi.org/10.1016/j.rse.2011.01.001), but some issues persist, such as very low LAI over boreal forests in the wintertime even after the data were reprocessed (e.g., Fig. 9C of Yuan et al., 2011).

To investigate these issues further, the 8-day MODIS Collection 6 (C6) LAI Product at 500-m resolution (MCD15A2H, https://lpdaac.usgs.gov/documents/2/mod15_user_guide.pdf) was examined for North America. Statistics were calculated for a 17-year period (2003-2019) by month and by land cover type as defined in the "Land Cover Type 1" Science Data Set (SDS) of the Collection 6 MODIS Land Cover (MCD12Q1) Product (https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf). Different data filtering options were tested, and comparisons were performed with North American LAI fields calculated independently from the Biogenic Emissions Landuse Database, version 4 (BELD4; https://www.epa.gov/air-emissions-modeling/biogenic-emission-sources and http://doi.org/10.5281/zenodo.3620734 ). This presentation will show results from this analysis, and will discuss possible approaches to address some current limitations of satellite-derived LAI.