MAX-DOAS observes scattered sunlight under various (mostly slant) elevation angles. From combinations of the retrieved NO2 slant column densities (SCDs) obtained at different elevation angles, information on the vertical NO2 profile and/or on the corresponding vertical column density (VCD) can be obtained (e.g. Hönninger et al., 2002; Sinreich et al., 2005; Wittrock et al., 2004; Wagner et al., 2011). Spectral calibration of the MAX-DOAS instruments was performed by fitting a measured spectrum to a convoluted solar spectrum based on a high-resolution solar spectrum (Kurucz et al., 1984). Several trace gas absorption cross-sections of NO2 at 294 K (Vandaele et al., 1996), H2O at 290 K (Rothman et al., 2005), Glyoxal at 296 K (Volkamer et al., 2005), O3 at 243 K (Bogumil et al., 2003) and O4 at 286 K (Hermans et al., 1999) were convolved to match the resolution of the instrument and then used in the spectral analysis using a wavelength range of 420–450 nm (also a Ring spectrum was included in the fitting process). The output of the spectral analysis is the NO2 SCD, which represents the NO2 concentration integrated along the corresponding light paths through the atmosphere.

Since a spectrum measured in the zenith direction (a so-called Fraunhofer reference spectrum) is included in the fit process to remove the strong Fraunhofer lines, the retrieved NO2 SCD actually represents the difference between the SCDs of the measurement and the Fraunhofer reference spectrum; it is usually referred to as differential SCD or DSCDmeas. The tropospheric DSCD for the elevation angle α can be derived from MAX-DOAS observation by subtracting the NO2 DSCD for the closest zenith observation α0=90∘): DSCDtropα=DSCDmeasα-DSCDmeasα0. DSCDs are converted into VCDs (the vertically integrated concentration) using so-called air mass factors (AMFs, Solomon et al., 1987), defined by AMF=SCD/VCD. In many cases AMFs are determined from radiative transfer simulations (Solomon et al., 1987). However, if trace gas column densities are retrieved from MAX-DOAS observations at high elevation angles (>10∘), the AMF can be determined by the so-called geometric approximation (Hönninger et al., 2002; Brinksma et al., 2008; Wagner et al., 2010): AMFtrop≈1sin⁡(α). In this study, the tropospheric vertical column density (VCDtrop) is obtained from DSCDtrop(α) as discussed by Wagner et al. (2010): VCDtrop=DSCDtropαAMFtrop(α-AMFtrop(α0)). During the field experiments, the MAX-DOAS instruments have been mounted on solid tables (aluminium structure) at approx. 11 m a.g.l. (NW natural forest, hotel station) and 3.5 m a.gr. (remainder of sites) with the telescope facing northwards. Observations were always made on elevation angles of 0∘, 2∘, 4∘, 6∘, 8∘, 10∘, 15∘, 20∘, 45∘ and 90∘. The VCDtrop were determined from measurements at 15∘. The potential importance of scattering on the interpretation of the MAX-DOAS measurements depends on two main aspects: first on the height of the trace gas layer and second on the amount of aerosols. In our case the trace gas layer is shallow and the aerosol amount is low (see Sect. 2.2.8). Thus scattering effects can be neglected. However, for comparison of the DSCDtrop data obtained by MAX-DOAS with the simulated SCDs obtained from 3-D distributions of NO2 concentration – calculated with LASAT (Lagrangian Simulation of Aerosol Transport) – on the basis of laboratory-derived net potential NO2 fluxes) the lower elevation angles (2∘, 4∘) for DSCDtrop(α) have been used, which have a much higher sensitivity to the observed NO2.

For classifying all MAX-DOAS measurements whether they were made upwind, downwind, or in the centre of Milan oasis, their observation position was related to the mean wind direction during each measurement period. Wind measurements were part of accompanying in situ measurements (see below).

Wind direction, wind speed, air temperature, relative humidity, barometric pressure and rainfall intensity were measured by combined weather sensors (weather transmitter WXT510, Vaisala, Finland). All five weather sensors were operated side-by-side for 1 week before being mounted at the individual measurement sites (1)–(5). Based on these results, all meteorological data measured between 3–24 July 2011 were corrected using one of the sensors as reference. All combined weather sensors' data, as well as those of net radiation (four-component net radiation sensor, model NR01, Hukseflux, the Netherlands) and soil temperature (thermistor probe, model 109, Campbell Scientific, USA) were recorded every minute. Ambient O3 concentrations and NO2 photolysis rates were also measured in situ; both quantities are necessary to calculate the NO→NO2 conversion factor (see Sect. 2.2.8). Ozone concentrations were measured by UV-absorption spectroscopy (model 49i, ThermoFisher Scientific, USA) and NO2 photolysis rate by a filter radiometer (model 2-Pi-JNO2, Metcon, Germany) at 1 min intervals.

Microbial processes responsible for biogenic NO emission are confined to the uppermost soil layers (Galbally and Johansson, 1989; Rudolph et al., 1996; Rudolph and Conrad, 1996). Consequently, composite soil samples (1 kg of top soil, 0–5 cm depth) were collected at the individual sites of Milan oasis (natural forest, cotton, jujube, cotton and jujube mixture, desert). All samples (air dried) were sent from Xinjiang to Germany by air cargo and stored refrigerated (+4 ∘C) prior to laboratory analysis of the net potential NO flux (see below). Sub-samples were analysed for dry bulk soil density (ISO 11272), pH (ISO 10390), electrical conductivity (salinity, ISO 11265), contents of nitrate and ammonium (ISO 14256), total carbon and total nitrogen (ISO 10649 and ISO 13878), texture (ISO 11277), as well as the soil water potential (pF values 1.8, 2.5, 4.2, Hartge and Horn, 2009).

Electrical conductivity varied between 1.6 and 9.5 dS m-1 within the managed soils, and was 59.8 and 3.0 dS m-1 in the natural forest and desert soils, respectively. Commercially available soil moisture probes – e.g. TDR (Time Domain Reflectometry) and FDR (Frequency Domain Reflectometry) – show extreme interferences for soils of > 2 dS m-1 (see Kargas et al., 2013) and their calibration for such soils is extremely challenging, if possible at all. Indeed, FDR signals monitored in Milan oasis' soils were extremely noisy and spurious. Nevertheless, up-scaling of the laboratory-derived net potential NO fluxes needs data of the uppermost layer of each soil of Milan oasis land types (see Sect. 2.2.6). For that, as the most reasonable approximation, it was decided to use the individual (constant) gravimetric soil moisture content which corresponds to the so-called “wilting point”. The latter was determined by laboratory water tension measurements (pF 4.2) on undisturbed soil cores from each land-cover type. The wilting point is defined as that soil moisture in the root zone which would cause irreversible wilting of plants. Wilting point conditions in the uppermost soil layers (2 cm) of soils in the Taklimakan Desert are easily reached, since evaporation is extremely high (evaporating capacity 2920 mm -1). Even after flooding irrigation of Milan oasis' crop fields, these conditions have repeatedly been observed within at least 3 days by visual inspections.

The methodology for the laboratory measurement of the NO flux from soil was developed at the end of the 1990s (Yang and Meixner, 1997) and has been continuously used during the last two decades (Otter et al., 1999; Kirkman et al., 2001; van Dijk and Meixner, 2001; Feig et al., 2008a; Feig et al., 2008b, 2009; Yu et al., 2008, 2010a, b; Ashuri, 2009; Gelfand et al., 2009; Bargsten et al., 2010). The methodology has been significantly improved in the frame of this study and is described in detail by Behrendt et al. (2014).

Generally, the release of gaseous NO from soil is the result of microbial NO production and simultaneous NO consumption. The latter is, as shown by Behrendt et al. (2014), particularly for arid and hyper-arid soils, negligible. Applying the laboratory dynamic chamber method, the release of NO is determined by incubating aliquots of the soil samples in a dynamic chamber system under varying, but prescribed conditions of soil moisture, soil temperature and chamber's headspace NO concentrations. From the difference of measured NO concentrations at the outlets of each soil-containing chamber and an empty reference chamber, actual net potential NO fluxes (in terms of mass of nitric oxide per area and time) is calculated as function of soil moisture and soil temperature. For that, a known mass (approx. 60 g dry weight) of sieved (2 mm) and wetted (to water holding capacity) soil is placed in one of six Plexiglas chambers (volume 9.7 × 10-4 m3) in a thermo-controlled cabinet (0–40 ∘C). After passing through a purification system (PAG 003, Ecophysics, Switzerland), dry pressurized, zero (i.e. “NO-free”) air is supplied to each chamber, controlled by a mass flow controller (4.167 × 10-5 m3 s-1). The outlet of each chamber is connected via a switching valve system to the gas-phase chemiluminescence NO analyser (model 42i-TL, Thermo Fisher Scientific Inc., USA) and to the non-dispersive infrared CO2/H2O analyser (model LI-COR 840A, LI-COR Biosciences Inc., USA). During a period of 24–48 h, the soil samples are slowly drying out, hence providing the desired variation over the entire range of soil moisture (i.e. from water-holding capacity to wilting point conditions and completely dry soil). During the drying-out period, the temperature of thermo-controlled cabinet is repeatedly changed from 20 to 30 ∘C, hence providing the desired soil temperature variation (Behrendt et al. 2014). Occasionally, nitric oxide standard gas (200 ppm) is diluted into the air purification system via a mass flow controller; this allows the control of the chamber headspace NO concentration when determining NO consumption rate of the soil sample. The actual soil moisture content of each soil sample is determined by considering the H2O mass balance of each chamber, where the temporal change of the chamber's headspace H2O concentration is explicitly related to the evaporation rate of the soil sample. Tracking the chamber's headspace H2O concentration throughout the drying-out period and relating it to the gravimetrically determined total soil mass at the start and end of the measurement period delivers the actual gravimetric soil moisture content of the soil sample (Behrendt et al., 2014).

As shown during the last two decades (Yang and Meixner, 1997; Otter et al., 1999; Kirkman et al., 2001; van Dijk and Meixner, 2001; van Dijk et al., 2002; Meixner and Yang, 2006; Yu et al., 2008, 2010; Feig et al., 2008; Ashuri, 2009; Feig, 2009; Gelfand et al., 2009 and Bargsten et al., 2010), the dependence of NO release from gravimetric soil moisture and soil temperature can be characterized by two explicit dimensionless functions, the so-called optimum soil moisture curve g(θg) and the exponential soil temperature curve h(Tsoil): g(θg)=θgθg,0aexp⁡-aθgθg,0-1hTsoil=exp⁡ln⁡Q10,NO10Tsoil-Tsoil,0, where θg is the dimensionless gravimetric soil moisture content, θg,0 the so-called optimum gravimetric soil moisture content (i.e. where the maximum NO release has been observed), a is the soil moisture curve's shape factor (solely derived from NO release and gravimetric soil moisture data which have been observed during the drying-out measurements, see Behrendt et al. 2014), Tsoil is the soil temperature (in ∘C), Tsoil,0 is the reference temperature (here: 20 ∘C) and Q10,NO is the (logarithmic) slope of h(Tsoil), defined by Q10,NO=ln⁡FNOθg,0,Tsoil,1-ln⁡FNOθg,0,Tsoil,0Tsoil,1-Tsoil,0, where Tsoil,1 is a soil temperature which is 10 K different from Tsoil,0 (here: 30 ∘C). The actual NO fluxes FNO (ngm-2s-1; in terms of mass of nitric oxide) are defined by FNOθg,0,Tsoil,0=QAsoilmNO,chamθg,0,Tsoil,0-mNO,reffC,NO FNOθg,0,Tsoil,1=QAsoilmNO,chamθg,0,Tsoil,1-mNO,reffC,NO, where Q is the purging rate of the dynamic chambers (m3 s-1), Asoil is the cross-section of the dynamic chamber (m2) and mNO,cham and mNO,ref are the NO mixing ratios (ppb) observed under conditions (θg,0, Tsoil,0) and (θg,0, Tsoil,1) at the outlets of each soil chamber and the reference chamber, respectively. The conversion of NO mixing ratios to corresponding NO concentrations (ng m-3, in terms of mass of nitric oxide) is considered by fC,NO (=572.5 ngm-3ppb-1 under STP conditions). Finally, the net potential NO flux, FNO(θg, Tsoil) is given by FNOθg,Tsoil=FNOθg,0,Tsoil,0gθghTsoil. This net potential NO flux is specific for each soil sample, hence for sites (1), (2), (4) and (5) of Milan oasis; the actual NO flux of the sites is calculated by applying corresponding field data of gravimetric soil moisture and soil temperature. This procedure has been successfully applied for a variety of terrestrial ecosystems (e.g. Otter et al., 1999; van Dijk et al., 2002; Ganzeveld et al., 2008). For soils of the Zimbabwean Kalahari (Ludwig et al., 2001; Meixner and Yang, 2006), for a German grassland soil (Mayer et al., 2011), but also for Brazilian rainforest soils (van Dijk et al., 2002), soil biogenic NO fluxes derived from the described laboratory incubation method have been successfully verified by field measurements using both field dynamic chamber and micrometeorological (aerodynamic gradient) techniques.

Image classification is likely to assemble groups of identical pixels found in remotely sensed data into classes that match the informational categories of user interest by comparing pixels to one another and to those of known identity. For the purposes of our study, land-cover classification was carried out based on two Quickbird images (0.6 m ground resolution, DigitalGlobe, http://www.digitalglobe.com) acquired on 09 April and 31 August 2007 respectively, with the aid of a recent ETM+ Landsat image (141/033, http://earthexplorer.usgs.gov/) acquired on 25 April 2011 (15 and 30 m spatial resolution). A major advantage of using Quickbird images of high spatial resolution images is that such data greatly reduce the mixed-pixel problem (a “mixed pixel” consists of several land-cover classes) and provide a greater potential to extract much more detailed information on land-cover structures (e.g. field borders, buildings, roads) than medium or coarse spatial resolution data, whether using on-screen digitizing or image classification.

However, we take advantage of resolution merge processing to increase the spatial resolution of the Landsat image from 30 to 15 m for the bands 1–5 and 7 for better land-cover mapping and to update the land-cover map from 2007 to 2011. Then, we defined different areas of interest (AOIs) to represent the major land covers with the aid of in situ GPS data collection (45 points). Next, we increased the number of AOIs based on the image spectral analysis method. After that, supervised classification was performed using the maximum likelihood parametric rule and probabilities. This classifier uses the training data by means of estimating means and variances of the classes, which are used to estimate Bayesian probability and also consider the variability of brightness values in each class. For that, it is the most powerful classification method when accurate training data are provided, and is one of the most widely used algorithms (Perumal and Bhaskaran, 2010). As a result, five major ecosystems were determined: cotton, jujube, cotton/jujube mixture fields, desert and plant cover. The cotton and the jujube fields are the most dominant types. Finally, the classified land-cover image was converted into vector format using polygon vector data type to be implemented in LASAT analysis as sources of NO flux and for the purpose of estimating NO concentrations. The map includes 2500 polygons of different sizes as sub-units of Milan major land cover.

The soil NO emission sources of Milan oasis were defined by individual source units, which have been identified as those sub-units (polygons) of the land-cover vector map consisting of natural forest or desert, or covered by cotton, jujube, cotton/jujube mixture. Two identifiers have been attributed to each source unit: (a) a metric coordinate whose numerical format refers to the corner of the corresponding polygon, and (b) a unique ID number followed by a description of its land-cover type. The soil NO source strength (i.e. actual NO flux, see Sect. 2.2.4) of each source unit has been calculated from the corresponding net potential NO flux, the land-cover type specific gravimetric soil moisture content (“wilting point”) and the actual soil temperature, which has been in situ measured for each of the land-cover type of Milan oasis (see Sect. 2.2.2). Those polygons not matching the mentioned land-cover types and other tiny polygons generated by digital image processing techniques were dismissed to avoid intricate geometric errors affecting NO emission data. In other words, these “other classes” were dissolved before performing LASAT analysis to avoid extreme values.

Having the actual NO source units of the Milan oasis available, the 3-D distribution of NO concentrations in the atmospheric boundary layer (0–1500 m a.gr.) over Milan oasis have been calculated by the Lagrangian dispersion model LASAT (German VDI Guidelines VDI3945, part 3; see Janicke Consulting, 2011). LASAT is a state-of-the-art model, since (a) LASAT is one of those transport dispersion models of air pollution which is officially licensed for legal use of environmental issues (in Germany), and (b) among comparable micro-scale (e.g. street canyons) transport-dispersion models LASAT considers at least chemical transformations of first order and still remains truly operational. Being a transport dispersion model, LASAT basically considers advection (“pixel cross-talk”) by applying the 3-D continuity equation for any chosen tracer (see German VDI Guidelines VDI3945, part 3, see Janicke Consulting, 2011). For that, pre-processing of meteorological parameters (i.e. 3-D wind distributions, based on meteorological in situ measurements, see Sect. 2.2.2) and calculation of dispersion parameters (σy, σz) have to be performed. Unfortunately, it was difficult to obtain fine resolution using LASAT individually. Therefore, the LASAT model was integrated with the Geographic Information System (ArcGIS) by using an advanced module, namely LASarc (IVU Umwelt GmbH, 2012). LASarc allowed us to calculate NO concentrations using relatively fine resolution of 30 m × 30 m and taking advantage of using the integrated map colour scheme in ArcGIS. This module has been used to realize Milan oasis' complex NO source configuration and to set up calculations of LASAT.

The model was designed to calculate NO concentrations at 16 different vertical layers (0–3, 3–5, 5–10, 10–20, 20–30, 30–50, 50–70, 70–100, 100–150, 150–200, 200–300, 300–400, 400–500, 500–700, 700–1000 and 1000–1500 m a.gr.). The horizontal resolution is 30 m, in both the x-direction (W–E) and the y-direction (S–N), which results in 656 (x) and 381 (y) grids for the Milan oasis domain. LASAT's meteorological input data contain a variety of parameters, namely start and end time (T1, T2), wind speed (Ua) and wind direction (Ra) at anemometer height (Ha), average surface roughness (Z0), and atmospheric stability (in terms of stability classes). These parameters have been provided in a time-dependent tabular form, updated every 30 minutes (except Z0). Average (30 min) wind speed and wind direction data have been calculated from in situ measurements (1 min resolution, see Sect. 2.2.2).

LASAT's pre-processing module determines the vertical profile of wind speed according to the well-known logarithmic relation Uz=u∗kln⁡zZ0, where U(z) is the horizontal wind speed (m s-1) at height z(m), u∗ is the friction velocity (ms-1), k is the dimensionless von Karman constant (= 0.4, Simiu and Scanlan, 1996) and Z0 is the surface roughness length (m). LASAT's pre-processing module accepts only one individual value for Z0; nevertheless, the required mean value has been calculated from all of Z0 of Milan oasis domain, which have been assigned to each of the sub-units (polygons) of the vector land-cover map (see Sect. 2.2.5). For individual Z0, we calculated land-cover specific NDVI data (normalized differential vegetation index) from Landsat ETM+ image (141/033): NDVI=rNIR-rREDrNIR+rRED, where NIR is the reflectance in the near-infrared bandwidth (0.77–0.90 µm) and RED is the reflectance in the red bandwidth (0.63–0.69 µm). In Landsat ETM+ images, these correspond to bands 4 and 3, respectively. Finally, rNIR and rRED are the corresponding ratios of reflected and incident energy as a function of wavelength (see Chander and Markham, 2003). Then, surface roughness grid data were estimated as Z0x,y=exp⁡axyNDVIx,y+bxy, where axy and bxy are constants, which are, according to Morse et al. (2000), derived from NDVI(x,y) and GPS(x,y) data for known sample pixels representing the earlier classified land-cover types, namely natural forest, desert, cotton, jujube, and cotton/jujube mixture. Corresponding land-cover type Z0 are 0.45, 0.01, 0.18, 0.26 and 0.22 m, respectively; the required average value over the entire LASAT model domain results in Z0=0.22 ± 0.158 m.

Besides mechanical turbulence (Z0), atmospheric stability most affects the dispersion of trace substances. For Milan oasis' atmospheric boundary layer, atmospheric stability has been calculated according to the “solar radiation/delta T (SRDT)” method in 30 min intervals. This method (see Turner, 1994) is widely accepted because of its simplicity and its representativeness for atmospheric stability over open country and rural areas, like the Milan oasis domain. Daytime stability classes are calculated from in situ measurements of solar radiation and horizontal wind speed (see Sect. 2.2.2).

Finally, 30 min means of all parameters and input variables of LASAT have been calculated. Using these, about 4 × 106 gridded data points of 3-D NO concentration have been calculated for each time period considered in Sect. 3.2.

There is only one tool to provide a robust relationship between biogenic soil NO emissions on the one hand and MAX-DOAS observed SCDs and VCDs on the other hand: the exact simulation of the MAX-DOAS measurement through spatial integration of 3-D NO concentrations calculated by LASAT (followed by NO→NO2 conversion). At a given location of the MAX-DOAS measurement, integration must be performed from the height where the MAX-DOAS instrument has been set up (hMAX-DOAS) to the end of the atmospheric boundary layer (hABL=1500 m a.gr.) along two virtual light paths: (a) the vertical-up path (VCD) and (b) the slant path (SCD), according to the selected elevation angle of each MAX-DOAS measurement.

Calculation of simulated VCD for NO (VCDNO,sim) at the location of a MAX-DOAS instrument is achieved as follows: (a) determination of the NO mass density (ng m-2) of the vertical column between hMAX-DOAS and hABL; this is obtained by adding NO concentrations (ng m-3 in terms of mass of nitric oxide) of all LASAT cells in the vertical direction over that 30 m × 30 m grid, which contains the location of the MAX-DOAS instrument, multiplied by the height differenceΔh=hABL-hMAX-DOAS (in m), (b) multiplying that NO mass density by the ratio of Avogadro's number (6.02217 × 1026 molecules kmol-1) and the molecular weight of NO (30.0061 × 1012 ng kmol-1) delivers the desired value of SCDNO,sim in units of molecules m-2 (× 10-4: molecules cm-2) at the location of the MAX-DOAS instrument. Calculation of simulated SCD for NO (SCDNO,sim) requires the determination of the 3-D light path through the he trace gas layer. Positioning of MAX-DOAS's telescope was always to the north; the selected MAX-DOAS elevation angle α and hABL deliver the length of the slant light path (=hABL/sin⁡α). The desired SCDNO,sim (in molecules m-2) results from the NO mass density of the slant column multiplied by the length of the slant light path, where the NO mass density is equivalent to the sum of all NO concentrations of those LASAT cells which are intersected by the slant light path from the position of the MAX-DOAS instrument to hABL.

For conversion of VCDNO,sim to VCDNO2,sim and SCDNO,sim to SCDNO2,sim it is assumed that the photostationary state (PSS) of the triad NO, NO2 and O3 is established in Milan oasis' atmospheric boundary layer. According to Leighton (1961) this chemical equilibrium state is due to fast photochemical reactions, namely NO+O3→NO2+O2 and NO2+hυ→NO+O, from which the so-called photostationary state NO2 concentration (NO2,PSS) can be derived as [NO2,PSS]=[O3][NO]k1j(NO2), where [O3] is the ozone number density (molecules cm-3; calculated from in situ measured O3 concentrations, see Sect. 2.2.2), [NO] is the NO number density, k1 is the reaction coefficient of the NO+O3→NO2+O2 reaction (cm3 molecules-1 s-1; Atkinson et al., 2004), and j(NO2) is the in situ measured NO2 photolysis rate (in s-1; see Sect. 2.2.2). Finally, VCDNO2,sim and SCDNO2,sim are calculated from VCDNO,sim and SCDNO,sim by VCDNO2,sim=CF0×VCDNO,simandSCDNO2,sim=CF0×SCDNO,sim, where the NO→NO2 conversion factor is defined by CF0=[O3]k1/j(NO2).

Since the NO–NO2–O3 photochemical equilibrium could not be handled by LASAT's “chemical” algorithm, we decided to use measured data (O3 mixing ratio, NO2 photolysis rate, see Sect. 2.2.2) to convert the calculated 3-D NO mixing ratio to the photo-stationary 3-D NO2 mixing ratio. For that, a constant vertical O3 mixing ratio (up to 1500 m a.gr.) is assumed over Milan oasis. This is justified by the fact that, particularly in arid and hyper-arid landscapes at midday conditions (maximum of insulation), the entire atmospheric boundary layer is intensively mixed, which is due to extensive convective heating of the surface by the sun which produces powerful buoyant thermals that establish the so-called mixing layer. Consequently, a uniform vertical mixing ratio is expected for trace gases with chemical lifetimes greater than the exchange time of the atmospheric boundary layer (see Husar et al. 1978; Stull, 1988). This assumption is valid for ozone. Vertically constant O3 mixing ratio has been reported for the atmospheric boundary layer over semi-arid southern Africa (Meixner et al., 1993). Concerning the vertical distribution of j(NO2), it is obvious that the downward component of the actinic flux increases with increasing elevation due to the decreasing optical thickness of the scattering air masses. However, the altitude effect on the actinic flux in the first kilometre of the troposphere is typically very small. Trebs et al. (2009) used the Tropospheric Ultraviolet Visible model to calculate the typical vertical change of the actinic flux and found a vertical gradient of 1.1 % km-1. Consequently, our calculations of the NO to NO2 conversion in the boundary layer over Milan oasis (1500 m a.gr.) have not considered any potential vertical change of the j(NO2) values measured at ground level. Nevertheless, for the case of our measurements the locally enhanced NO values caused by the soil emissions have a small but systematic effect on the ozone concentration, and thus also on the Leighton ratio: close to the surface (below about 50 m) the NO concentrations can be quite large, with maximum values up to about 10 ppb. Consequently, the ozone concentration will be reduced due to the reaction with NO by up to about 10 ppb. This means that the Leighton ratio will be reduced by up to about 25 %. Although the reduction of the ozone mixing ratio will be partly compensated by mixing with air from higher altitudes, the simulated NO2 mixing ratios might overestimate the true NO2 mixing ratios by up to about 25 %. Probably the true overestimation for our measurements is much smaller because the typical NO mixing ratio within the lowest 100 m is much lower than 10 ppb.