Mapping recession risk for cultural heritage stone in Mexico City due to dry and wet deposition of urban air pollutants

Abstract. The shipping sector contributes significantly to increasing emissions of air pollutants. In order to achieve sustainable shipping, primarily through new regulations and techniques, greater knowledge of dispersion and deposition of air pollutants is required. Regional model calculations of the dispersion and deposition of sulphur, nitrogen and particulate matter from the international maritime sector in the Baltic Sea and the North Sea have been made for the years 2009 to 2013. In some areas in the Baltic Sea region the contribution of sulphur dioxide, nitrogen oxide and nitrogen dioxide from international shipping represented up to 80 % of the total near surface concentration of the pollutants. Contributions from shipping of PM2,5 and PM10 were calculated to a maximum of 21 % and 13 % respectively. The contribution of wet deposition of sulphur from shipping was maximum 29 % of the total wet deposition, and for dry deposition the contribution from shipping was maximum 84 %. The highest percentage contribution of wet deposition of nitrogen from shipping reached 28 % and for dry deposition 47 %. The highest concentrations and deposition of the pollutants in the study were found near large ports and shipping lanes. High concentrations were also found over larger areas at sea and over land where many people are exposed. With enhanced regulations for sulphur content in maritime fuel, the cleaning of exhausts through scrubbers has become a possible economic solution. Wet scrubbers meet the air quality criteria but their consequences for the marine environment are largely unknown. The resulting potential of future acidification in the Baltic Sea, both from atmospheric deposition and from open-loop scrubber water along the shipping lanes, based on different assumptions about sulphur content in fuel and scrubber usage has been assessed. Shipping is expected to increase globally and in the Baltic Sea region, deposition of sulphur due to shipping will depend on traffic density, emission regulations and technology choices for the emission controls. To evaluate future changes scenarios are developed considering the amount of scrubber technology used. The increase in deposition for the different scenarios differs slightly for the basins in the Baltic Sea. The proportion of ocean acidifying sulphur from ships increases when taking scrubber water into account and the major reason to increasing acidifying nitrogen from ships are due to increasing ship traffic. This study also generates a database of scenarios for atmospheric deposition and scrubber exhaust from the period 2011 to 2050.

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Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17062025 ◽

2020 ◽

Vol 17 (6) ◽

pp. 2025

Author(s):

Laura Goulier ◽

Bastian Paas ◽

Laura Ehrnsperger ◽

Otto Klemm

Keyword(s):

Air Pollutants ◽

Air Pollutant ◽

Urban Air ◽

Pollutant Concentrations ◽

Day Of The Week ◽

Artificial Neural ◽

Input Variables ◽

Air Pollutant Concentrations ◽

Artificial Neural Network Ann ◽

Performance Results

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

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Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

10.5194/acp-2020-1169 ◽

2020 ◽

Author(s):

Shibao Wang ◽

Yun Ma ◽

Zhongrui Wang ◽

Lei Wang ◽

Xuguang Chi ◽

...

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Low Cost ◽

Air Pollutant ◽

Mitigation Strategies ◽

Traffic Emissions ◽

Urban Air ◽

Industrial Emissions ◽

Gis Technology ◽

Mobile Monitoring

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyper-local scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (Oct. 2019–Sep. 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. While the O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

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Intervention analysis with autoregressive integrated moving average models for time series of urban air pollutants

Journal of Physics Conference Series ◽

10.1088/1742-6596/2139/1/012002 ◽

2021 ◽

Vol 2139 (1) ◽

pp. 012002

Author(s):

L A Manco-Perdomo ◽

L A Pérez-Padilla ◽

C A Zafra-Mejía

Keyword(s):

Air Pollutants ◽

Moving Average ◽

Air Pollutant ◽

Urban Air ◽

Intervention Analysis ◽

Autoregressive Integrated Moving Average ◽

Pollutant Concentrations ◽

Moving Average Models ◽

Regulatory Interventions ◽

Air Pollutant Concentrations

Abstract The objective of this paper is to show an intervention analysis with autoregressive integrated moving average models for time series of air pollutants in a Latin American megacity. The interventions considered in this study correspond to public regulations for the control of urban air quality. The study period comprised 10 years. Information from 10 monitoring stations distributed throughout the megacity was used. Modelling showed that setting maximum emission limits for different pollution sources and improving fuel were the most appropriate regulatory interventions to reduce air pollutant concentrations. Modelling results also suggested that these interventions began to be effective between the first 4 days-15 days after their publication. The models developed on a monthly timescale had a short autoregressive memory. The air pollutant concentrations at a given time were influenced by the concentrations of up to three months immediately preceding. Moving average term of the models showed fluctuations in time of the air pollutant concentrations (3 months - 14 months). Within the framework of the applications of physics for the air pollution control, this study is relevant for the following findings: the usefulness of autoregressive integrated moving average models to temporal simulate air pollutants, and for its suitable performance to detect and quantify regulatory interventions.

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