Traffic density, wind and air stratification influence concentrations of air pollutant NO2

Leipzig researchers use a calculation method to remove weather influences from air pollution data

traffic air pollutant nitrogen dioxide COVID-19
Traffic density, wind and air stratification influence the pollution with the air pollutant nitrogen dioxide, according to the conclusion of a TROPOS study commissioned by the LfULG. Credits: Burkhard Lehmann, LfULG

Leipzig/Dresden. In connection with the effects of the COVID-19 pandemic, satellite measurements made headlines showing how much the air pollutant nitrogen dioxide (NO2) had decreased in China and northern Italy.  In Germany, traffic density is the most important factor. However, weather also has an influence on NO2 concentrations, according to a study by the Leibniz Institute for Tropospheric Research (TROPOS), which evaluated the influence of weather conditions on nitrogen dioxide concentrations in Saxony 2015 to 2018 on behalf of the Saxon State Office for Environment, Agriculture and Geology (LfULG). It was shown that wind speed and the height of the lowest air layer are the most important factors that determine how much pollutants can accumulate locally.

In order to determine the influence of various weather factors on air quality, the team used a statistical method that allows meteorological fluctuations to be mathematically removed from long-term measurements. The air quality fluctuates, in some cases very strongly, due to different emissions and the influence of the weather. Until now, however, it has been difficult to estimate, what share legal measures such as low emission zones or diesel driving bans have and what share the weather influences have in the actual air quality? With the method used, this will be easier in the future.

Nitrogen dioxide (NO2) is an irritant gas which attacks the mucous membrane of the respiratory tract, causes inflammatory reactions as an oxidant and increases the effect of other air pollutants. As a precursor substance, it can also contribute to the formation of particulate matter. Limit values have been set in the EU to protect the population: For nitrogen dioxide, an annual average value of 40 micrograms per cubic metre of air applies (μg/m³). To protect the health of the population, measures must be taken if these limit values are not complied with. In 2018/2019, for example, various measures were taken in Germany, ranging from a reduction in the number of lanes (e.g. in Leipzig) to driving bans for older diesel vehicles (e.g. in Stuttgart).

To evaluate the effectiveness of such measures, it would be helpful to determine the exact influence of weather conditions. The Saxon State Office for Environment, Agriculture and Geology (LfULG) therefore commissioned TROPOS to carry out a study on the influence of weather factors on NO2 concentrations and provided its measurement data from the Saxon air quality measurement network and meteorological data for this purpose. The researchers were thus able to evaluate data from 29 stations in Saxony over a period of four years, which represent a cross-section of air pollution – from stations at traffic centres to urban and rural background stations and stations on the ridge of the Erzgebirge mountains. They also calculated the height of the lowest layer in the atmosphere and incorporated data from traffic counting stations in Leipzig and Dresden into the study. A method from the field of machine learning was used for the statistical modelling, the application of which in the field of air quality was first published by British researchers in 2009.

In this way, the study was able to demonstrate that the traffic density at all traffic stations is most significantly responsible for nitrogen oxide concentrations. However, two weather parameters also have a significant influence on nitrogen dioxide concentrations: wind speed and the height of the so-called mixing layer. The latter is a meteorological parameter that indicates the height to which the lowest layer of air, where the emissions mix, extends. “It was also shown that high humidity can also reduce the concentration of nitrogen dioxide, which could be due to the fact that the pollutants deposit more strongly on moist surfaces. However, the exact causes are still unclear,” says Dominik van Pinxteren.

The statistical analysis has also enabled the researchers to remove the influence of the weather from the time series of pollutant concentrations: Adjusted for the weather, the concentration of nitrogen oxides (NOx) decreased by a total of 10 micrograms per cubic meter between 2015 and 2018 on average over all traffic stations in Saxony. In urban and rural areas and on the ridge of the Erzgebirge, however, NOx concentrations tend to remain at the same level. Even though there have been some improvements in air quality in recent years, there are good scientific arguments for further reducing air pollution.

In a way, this also applies to premature conclusions from the corona crisis: in order to find out how strong the influence of the initial restrictions on air quality actually was, the influence of the weather would have to be statistically removed in a longer series of measurements. To this end, investigations for the Leipzig area are currently underway at TROPOS, as is a Europe-wide study of the EU research infrastructure for short-lived atmospheric constituents such as aerosol, clouds and trace gases (ACTRIS), the German contribution to which is coordinated by TROPOS.


Dominik van Pinxteren, Sebastian Düsing, Alfred Wiedensohler, Hartmut Herrmann (2020): Meteorological influences on nitrogen dioxide: Influence of weather conditions and weathering on nitrogen dioxide concentrations in outdoor air 2015 to 2018. Series of publications of the LfULG, issue 2/2020 (in German only)
This study was commissioned by the State Office for Environment, Agriculture and Geology (LfULG).


LfULG-Projekt „Meteorologische Einflüsse auf Stickstoffdioxid“:


Press release on traffic density, wind and air stratification influence concentrations of air pollutant NO2 by Tilo Arnhold from the Leibniz Institute for Tropospheric Research (TROPOS)

Predicted versus observed epidemic curves over time. (copyright: Nature) Our model aggregates population outflow from Wuhan from January 1 to 24, 2020 to provide a reference growth pattern (i.e. epidemic curves) for COVID-19’s spread. Differences in the predicted and confirmed growth in confirmed cases can signal higher levels of COVID-19 community transmission.

An international research team led by the University of Hong Kong (HKU) developed a new method to accurately track the spread of COVID-19 using population flow data, and establishing a new risk assessment model to identify high-risk locales of COVID-19 at an early stage, which serves as a valuable toolkit to public health experts and policy makers in implementing infectious disease control during new outbreaks.  The study findings have been published in the journal Nature today (April 29).

Dr. Jayson Jia, Associate Professor of Marketing at the Faculty of Business and Economics of HKU and lead author of the study, and his co-authors used nation-wide data provided by a major national carrier in China to track population movement out of Wuhan between 1 January and 24 January 2020, a period covering the annual Chunyun mass migration before the Chinese Lunar New Year to a lockdown of the city to contain the virus. The movement of over 11 million people travelling through Wuhan to 296 prefectures in 31 provinces and regions in China were tracked.

Differing from usual epidemiological models that rely on historical data or assumptions, the team used real-time data about actual movements focusing on aggregate population flow rather than individual tracking. The data include any mobile phone user who had spent at least 2 hours in Wuhan during the study period.  Locations were detected once users had their phones on. As only aggregate data was used and no individual data was used, there was no threat to consumer privacy.

Combining the population flow data with the number and location of COVID-19 confirmed cases up to 19 February 2020 in China, Dr Jia’s team showed that the relative quantity of human movement from the disease epicentre, in this case, Wuhan, directly predicted the relative frequency and geographic distribution of the number of COVID-19 cases across China. The researchers found that their model can explain 96% of the distribution and intensity of the spread of COVID-19 across China statistically.

COVID-19 big data
Illustrative example of using model to track COVID-19 community spread risk. (copyright: Nature) Our model uses population movement to predict expected cases. The predicted spread of the SARS-CoV-2 virus can be used as a benchmark to identify which locales are ‘outliers’, which have significantly more or less cases than expected (given the movement data). The graph is an illustration of what our model showed on January 29. Prefectures to the left of the dashed line are outliers that have significantly more than expected cases, i.e., a higher level of unexplained or community transmission. Our model identified Wenzhou as having the most severe community transmission risk on January 29, 2020. The government announced a full quarantine of the prefecture on February 2, 2020.

The research team then used this empirical relationship to build a new risk detection toolkit. Leveraging on the population flow data, the researchers created an “expected growth pattern” based on the number of people arriving from the risk source, i.e. the disease epicentre. The team thereby developed a new risk model by contrasting expected growth of cases against the actual number of confirmed cases for each city in China, the difference being the “community transmission risk”.

“If there are more reported cases than the model expected, there is a higher risk of community spread. If there are fewer reported cases than the model expected, it means that the city’s preventive measures are particularly effective or it can indicate that further investigation by central authorities is needed to eliminate possible risks from inaccurate measurement,” explained Dr Jia.

“What is innovative about our approach is that we use misprediction to assess the level of community risk.  Our model accurately tells us how many cases we should expect given travel data.  We contrast this against the confirmed cases using the logic that what cannot be explained by imported cases and primary transmissions should be community spread. ” He added.

The approach is advantageous because it requires no assumptions or knowledge of how or why the virus spreads, is robust to data reporting inaccuracies, and only requires knowledge of relative distribution of human movement. It can be used by policy makers in any nation with available data to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing disease outbreaks.

“Our research indicates that geographic flow of people outperforms other measures such as population size, wealth or distance from the risk source to indicate the gravity of an outbreak.” said Dr Jia.

Dr Jia is currently exploring with fellow researchers the feasibility of applying this toolkit to other countries, and extending it to situations where there are multiple COVID-19 epicentres. The team is working with other national telecom carriers and seeking additional data partners.



The study’s co-authors are Jianmin Jia, Presidential Chair Professor at the Chinese University of Hong Kong, Shenzhen (corresponding author); Nicholas A. Christakis, Sterling Professor of Social and Natural Science at Yale; Xin Lu, the National University of Defense Technology in Changsha, China, and the Karolinska Institutet in Stockholm, Sweden; Yun Yuan, Southwest Jiaotong University; Ge Xu, Hunan University of Technology and Business.

Press release from The University of Hong Kong.

Plastic is a kind of widely used artificial material. The invention of plastic gives us a lightweight, strong and inexpensive material to use but also bring us the plastic apocalypse. Many of the unrecycled plastic waste ends up in the ocean, Earth’s last sink. Broken by waves, sunlight and marine animal, a single plastic bag can be broken down into 1.75 million microscopic fragments, which is called microplastics. Those microplastics might finally end up in our blood and system through the fish we eat or the water we drink.

During the long-term evolution of most plants on the earth, cellulose-based materials have been developed as their own structural support materials. Cellulose in plants mainly exists in the form of cellulose nanofibers (CNF), which have excellent mechanical and thermal properties. CNF, which can be derived from plant or produced by bacteria, is one of the most abundant all-green resources on Earth. CNF is an ideal nanoscale building block for constructing macroscopic high-performance materials, as it has higher strength (2 GPa) and modulus (138 GPa) than Kevlar and steel and lower thermal expansion coefficient (0.1 ppm K-1) than silica glass. Based on this bio-based and biodegradable building block, the construction of sustainable and high-performance structural materials will greatly promote the replacement of plastic and help us avoid the plastic apocalypse.

plastic substitute cellulose nanofiber plate
The cellulose nanofiber-derived bulk CNFP structural material and its characterization. (a) Photograph of large-sized CNFP with a volume of 320 × 220 × 27 mm3. (b) The robust 3D nanofiber network of CNFP. Numerous CNFs are intertwined with each other and combined together by hydrogen bonds. (c) Parts with different shapes of CNFP produced by a milling machine. (d) Ashby diagram of thermal expansion versus specific strength for CNFP compared with typical polymers, metals, and ceramics. (e) Ashby diagram of thermal expansion versus specific impact toughness for CNFP compared with typical polymers, metals, and ceramics. Copyright 2020, American Association for the Advancement of Science. Credit: Shu-Hong Yu

Nowadays, a team lead by Prof. Shu-Hong Yu from the University of Science and Technology of China (USTC) report a high-performance sustainable structural material called cellulose nanofiber plate (CNFP) (Fig. 1a and c) which is constructed from bio-based CNF (Fig. 1b) and ready to replace the plastic in many fields. This CNFP has high specific strength (~198 MPa/(Mg m-3)), which is 4 times higher than that of steel and higher than that of traditional plastic and aluminum alloy. In addition, CNFP has higher specific impact toughness (~67 kJ m-2/(Mg m-3)) than aluminum alloy and only half of its density (1.35 g cm-3).

Unlike plastic or other polymer based material, CNFP exhibit excellent resistance to extreme temperature and thermal shock. The thermal expansion coefficient of CNFP is lower than 5 ppm K-1 from -120 °C to 150 °C, which is close to ceramic materials, much lower than typical polymers and metals. Moreover, after 10 times of rapid thermal shock between 120 °C bake oven and -196 °C liquid nitrogen, CNFP remain its strength. Those result shows its outstanding thermal dimensional stability, which allow CNFP to own great potentials used as structural material under extreme temperature and alternate cooling and heating. Owing to its wide range of raw materials and bio-assisted synthesis process, CNFP is a kind of low-cost material with the cost of only 0.5 $/kg, which is lower than most of plastic. With low density, outstanding strength and toughness, and great thermal dimensional stability, all of those properties of CNFP surpass those of traditional metals, ceramics and polymers (Fig. 1d and e), making it a high-performance and environmental-friendly alternative for engineering requirement, especially for aerospace application.

CNFP not only has the power to replace plastic and saves us from drowning in them, but also has great potential as the next generation of sustainable and lightweight structural material.


Press release from the University of Science and Technology of China