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Locating and quantifying multiple landfills methane emission using aircraft

Chapter 3: Estimate of methane emissions from landfills: an approach based on

3.1 Locating and quantifying multiple landfills methane emission using aircraft

Emission of methane (CH4) into the atmosphere is a serious environmental issue associated with the landfilling of municipal waste, especially when landfills are not equipped with biogas recovery systems. This study is conducted on a 5x5 km spatial domain between the Naples and Caserta districts, in an area known as “terra dei fuochi”, in which four different illegal landfills are encompassed.

Figure 3.1 Location (panel A) of the four landfills (red polygons) inside the study domain (blue rectangle). Panel B shows the detail of the emissive area, with the surface extension.

The surroundings are well known in the media: urban, industrial and toxic waste were spilled and dumped in old dismissed quarries or buried without any control or safety measures for decades. This led to a patchy presence of waste with consequent spread CH4 sources with several emitting hot spots. The heterogeneous landscape that characterizes this area, in combination with the widespread presence of CH4 sources, make it a difficult spot for the assessment of CH4 emission using the common measurements techniques. The presence of several hot spots with different magnitude

68 of emission poses a problem for chamber measurement techniques, as they become less representative due to low spatial coverage of the measurements and their temporal discontinuity. The eddy covariance technique is generally suitable for the measurement of CH4 emissions from landfill, but the orography and the presence of four distinct landfills closely located within the domain affect the application of this technique as well.

Figure 3.2 Typical flight path of the Sky Arrow ERA during the measurement campaigns above the landfills.

To overcome these issues the airborne platform Sky Arrow ERA, equipped with the biogas payload described in Chapter 1, has been used for the estimate of landfill emissions. Eight flights were performed above the landfills following the same flight path, a series of grids repeated at different heights above the landfills as shown in figure 3.2. Flying grids instead of flying a circle or a polygon around the sources (as performed in the classical mass balance approach) allow to reconstruct both methane densities and wind components not only at the edge of the study area, but also inside the domain.

Figure 3.3 shows the typical trend of CH4 mixing ratios versus heights measured during each flight. Multiple strong plumes were measured above the study area at

69 different heights; the CH4 signal above the landfills decreased gradually with altitude, although during daytime convective conditions, mixing ratios greater than background values were still measured even at the highest level, confirming the presence of strong emission sources at the ground.

Figure 3.3Time series of CH4 mixing ratio measured during one of the flights at 1 Hz resolution. Flight pattern portions at constant height correspond to the grids performed above the study domain.

CH4 densities and wind data were then interpolated on a grid encompassing the flight domain, extending vertically from the surface to an altitude at which plumes are no longer sampled (ZTOP). Gridded data were computed at 50 meter horizontal resolution and 20 meter vertical resolution, using an inverse distance weighting (IDW) to a power (squared) algorithm. This algorithm is based solely on the assumption that close data points are more related to each other than distant points, not relying on any spatial or temporal relationship:

where Cj est is the estimated value of CH4 densities or wind data for location j, Ci are the values of the neighbouring points i and d2 the squared distance between grid note

Equation 3.1

70 j and neighbouring points. Following equation 3.1, we obtained a gridded dataset of CH4 density and wind speed.

Figure 3.4CH4 density map integrated along the z direction obtained from one of the flights (F6) after 3D interpolation. The dimensions of the study area are reported on x and y axes.

Black lines represent the flight paths above the landfills. The grid is rotated to align with the mean wind direction.

Wind and CH4 density grids were rotated for each flight, according to the mean wind direction of each flight, obtaining a wind-aligned box domain (Fig. 3.4). The CH4 net mass flow was then obtained multiplying gridded densities by rotated wind speed. The total mass flow (MF) (g s-1) along and across the wind aligned direction was then calculated as the integral of the net MF along y-z (parallel to mean wind direction) and

71 along x-z (perpendicular to mean wind direction) using equations (3.2) and (3.3), to obtain MF along and across wind direction, respectively:

where ZTOP is the top height of the box, x1-x2 and y1 -y2 are the horizontal boundaries of the study area (5x5 km domain), dy dx and dz are horizontal and vertical grid spacing respectively (dy,dx= 50m, dz= 20m). Cij and Vij are the CH4 molar densities and wind speed, where i and j are horizontal and vertical grid cell indices. Total flow rates obtained from equations (3.2) and (3.3) were calculated for each flight based only on aircraft measurements, with no information or assumption on the underlying sources.

A steady-state Gaussian dispersion model was deployed to compute CH4 density C(x,y,z) at any point of the study domain as follows:

where x and y represent the downwind and the crosswind distance from the emitter, and z the vertical distance from the ground. The two Gaussian exponential terms describe the dispersion of the plume in the horizontal and vertical direction respectively, Q is the emission rate of the source that is considered constant in time and magnitude, L is the height of the emitter and U is the wind speed that defines the x direction. The meteorological conditions used as inputs for the Gaussian model (mixing height, temperature, wind speed and direction) were obtained for each flight directly from the aircraft observations, by considering the mean values measured during the lowest grid performed above the landfills. The Gaussian model was then applied separately for each landfill on the same grid described above for the actual measurements. Each surface grid cell (50 x 50 m) included in any landfill area was considered an emissive cell at location x = y = 0, with a point source at the centre of

Equation 3.2

Equation 3.3

Equation 3.4

72 the square. By then multiplying the gridded CH4 density - obtained from the Gaussian model - and the wind, we obtained CH4 mass flows associated to each landfill, and unit grid cell emission. Those mass flows were then integrated along z and subsequently along both x and y to retrieve, for each landfill, 1-d modeled mass flow (MMFi, where i is the number of landfills) signatures along and across wind direction, similarly to those computed from observations (figure 3.5).

An optimization approach by General Linear Model (GLM) was used to separate the contribution of each individual landfill through the equation:

where i = 4 is the number of landfills, MF is the CH4 mass flow calculated for each flight over the entire domain, obtained with equations (3.2) and (3.3), and MMFi are the flight specific modelled mass flows for each landfill. Equation (3.5) allows the estimation of landfill emission coefficients i that minimize the absolute difference between total measured mass flow (MF) and total modelled mass flow (4𝑖=1𝑀𝑀𝐹𝑖 𝛼𝑖). Emission rates for each individual source were then obtained by multiplying 1-d modeled mass flow by its relative coefficient i.

Equation 3.5

73 Figure 3.5 Comparison between modeled and measured mass flows. Black lines show computed mass flows for the study area down (upper panel) and across (lower panel) wind for each flight. The red lines show the estimates of CH4 mass flows obtained from the Gaussian dispersion model through the solution of equation 3.5

Emission partitioning shown in figure 3.6 revealed that S1 was the strongest CH4

source, with a mean emission covering 40% of the total and ranging from 30.7 ± 2.6 g m-1 day-1 (F1) to 63.1 ± 3.1 g m-1 day-1 (F8). S2 accounted for almost 30% of total emissions and values were quite steady for all flights, ranging between 31 and 48 g m

-1 day-1. For F8 no emission was estimated from S2, as this flight was characterized by

74 a single and strong CH4 plume located downwind of S1 and partially S3. S3 and S4 were the weaker sources accounting for 12% and 19% of the total emission. Emissions from S3 ranged from 54.0 ± 19.1 g m-1 day-1 (F1) to 0.0 ± 14. g m-1 day-1 (F4); for S4 the maximum emission was recorded during F2 (58.3 ± 27.1 g m-1 day-1) while the minimum during F8 (3.0 ± 9.8 g m-1 day-1).

Figure 3.6 Emissions partitioning of each flight among the different landfills.

Results of this study have been published in August 2019, in the paper “Locating and quantifying multiple landfills methane emission using aircraft data”, which can be found in Appendix A of this chapter.

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Appendix A

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