diff --git a/PySDM/attributes/isotopes/bolin_numbers.py b/PySDM/attributes/isotopes/bolin_numbers.py
index 8c63d4d409..88cb7dfe11 100644
--- a/PySDM/attributes/isotopes/bolin_numbers.py
+++ b/PySDM/attributes/isotopes/bolin_numbers.py
@@ -1,6 +1,6 @@
 """
 Derived droplet attribute representing the Bolin number for heavy
-water isotopologues (D, 17O, 18O substitutions).
+water isotopologues (2H, 17O, 18O substitutions).
 
 The Bolin number is a dimensionless coefficient relating the
 heavy-isotope mole tendency to the bulk liquid-water mass tendency
@@ -24,6 +24,7 @@ def __init__(self, builder, *, heavy_isotope: str):
         heavy_isotope : str
             Heavy isotopologue identifier (entry of ``HEAVY_ISOTOPES``).
         """
+        self.isotope = heavy_isotope
         self.moles_heavy = builder.get_attribute(f"moles_{heavy_isotope}")
         self.moles_light = builder.get_attribute("moles light water")  # TODO #1787
         self.cell_id = builder.get_attribute("cell id")
@@ -41,9 +42,10 @@ def recalculate(self):
         self.particulator.backend.bolin_number(
             output=self.data,
             cell_id=self.cell_id.data,
+            isotope=self.isotope,
             relative_humidity=self.particulator.environment["RH"],
             temperature=self.particulator.environment["T"],
-            density_dry_air=self.particulator.environment["dry_air_density"],
+            density_dry_air=self.particulator.environment["rhod"],
             moles_light_molecule=self.moles_light.data,
             moles_heavy=self.moles_heavy.data,
             molality_in_dry_air=self.molality_in_dry_air,
diff --git a/PySDM/backends/impl_numba/methods/isotope_methods.py b/PySDM/backends/impl_numba/methods/isotope_methods.py
index 4d17cb204f..cf057d263a 100644
--- a/PySDM/backends/impl_numba/methods/isotope_methods.py
+++ b/PySDM/backends/impl_numba/methods/isotope_methods.py
@@ -76,9 +76,7 @@ def body(
                     dm_heavy = dm_total[sd_id] / Bo * mass_ratio_heavy_to_total
                 dn_heavy_molecule = dm_heavy / molar_mass_heavy_molecule
                 moles_heavy_molecule[sd_id] += dn_heavy_molecule
-                mass_of_dry_air = (
-                    dry_air_density[cell_id[sd_id]] * cell_volume[cell_id[sd_id]]
-                )
+                mass_of_dry_air = dry_air_density[cell_id[sd_id]] * cell_volume
                 molality_in_dry_air[cell_id[sd_id]] -= (
                     dn_heavy_molecule * multiplicity[sd_id] / mass_of_dry_air
                 )
@@ -102,7 +100,7 @@ def isotopic_fractionation(
         """Update heavy-isotope composition during droplet growth/evaporation."""
         self._isotopic_fractionation_body(
             cell_id=cell_id.data,
-            cell_volume=cell_volume.data,
+            cell_volume=cell_volume,
             multiplicity=multiplicity.data,
             dm_total=dm_total.data,
             signed_water_mass=signed_water_mass.data,
@@ -133,6 +131,7 @@ def body(
             *,
             output,
             cell_id,
+            isotope,
             relative_humidity,
             temperature,
             density_dry_air,
@@ -148,29 +147,40 @@ def body(
                 conc_vap_total = (
                     pvs_water * relative_humidity[cell_id[i]] / ff.constants.R_str / T
                 )
-                rho_v = pvs_water / T / ff.constants.Rv
-
                 isotopic_fraction = ff.trivia__isotopic_fraction(
                     molality_in_dry_air=molality_in_dry_air[cell_id[i]],
                     density_dry_air=density_dry_air[cell_id[i]],
                     total_vap_concentration=conc_vap_total,
                 )
-                D_ratio_heavy_to_light = (
-                    ff.isotope_diffusivity_ratios__ratio_2H_heavy_to_light(T)
-                )
+                if isotope == "2H":
+                    D_ratio = ff.isotope_diffusivity_ratios__ratio_2H_heavy_to_light(T)
+                    alpha = ff.isotope_equilibrium_fractionation_factors__alpha_l_2H(T)
+                if isotope == "3H":
+                    D_ratio = ff.isotope_diffusivity_ratios__ratio_3H_heavy_to_light(T)
+                    alpha = ff.isotope_equilibrium_fractionation_factors__alpha_l_3H(T)
+                elif isotope == "18O":
+                    D_ratio = ff.isotope_diffusivity_ratios__ratio_18O_heavy_to_light(T)
+                    alpha = ff.isotope_equilibrium_fractionation_factors__alpha_l_18O(T)
+                elif isotope == "17O":
+                    D_ratio = ff.isotope_diffusivity_ratios__ratio_17O_heavy_to_light(T)
+                    alpha = ff.isotope_equilibrium_fractionation_factors__alpha_l_17O(T)
+
                 output[i] = ff.isotope_relaxation_timescale__bolin_number(
-                    D_ratio_heavy_to_light=D_ratio_heavy_to_light,
-                    alpha=ff.isotope_equilibrium_fractionation_factors__alpha_l_2H(T),
-                    D_light=ff.constants.D0,
+                    D_ratio_heavy_to_light=D_ratio,
+                    alpha=alpha,
                     Fk=ff.drop_growth__Fk(
                         T=T, K=ff.constants.K0, lv=ff.constants.l_tri
                     ),
+                    Fd=ff.drop_growth__Fd(
+                        T=T,
+                        D=ff.constants.D0,
+                        pvs=pvs_water,
+                    ),
                     R_vap=ff.trivia__isotopic_ratio_assuming_single_heavy_isotope(
                         isotopic_fraction
                     ),
                     R_liq=moles_heavy_atom / moles_light_isotope,
                     relative_humidity=relative_humidity[cell_id[i]],
-                    rho_v=rho_v,
                 )
 
         return body
@@ -180,6 +190,7 @@ def bolin_number(
         *,
         output,
         cell_id,
+        isotope,
         relative_humidity,
         temperature,
         density_dry_air,
@@ -191,6 +202,7 @@ def bolin_number(
         self._bolin_number_body(
             output=output.data,
             cell_id=cell_id.data,
+            isotope=isotope,
             relative_humidity=relative_humidity.data,
             temperature=temperature.data,
             density_dry_air=density_dry_air.data,
diff --git a/PySDM/backends/impl_thrust_rtc/methods/isotope_methods.py b/PySDM/backends/impl_thrust_rtc/methods/isotope_methods.py
index 1b04b33ec1..28b281f3b4 100644
--- a/PySDM/backends/impl_thrust_rtc/methods/isotope_methods.py
+++ b/PySDM/backends/impl_thrust_rtc/methods/isotope_methods.py
@@ -63,7 +63,7 @@ def __isotopic_fractionation(self):
 
             moles_heavy_molecule[i] += dn_heavy;
 
-            real_type mass_of_dry_air = dry_air_density[cid] * cell_volume[cid];
+            real_type mass_of_dry_air = dry_air_density[cid] * cell_volume;
 
             atomicAdd((real_type*) &molality_in_dry_air[cid], -dn_heavy * multiplicity[i] / mass_of_dry_air);
             """.replace("real_type", self._get_c_type()),
@@ -87,7 +87,7 @@ def isotopic_fractionation(
             n=len(multiplicity),
             args=(
                 cell_id.data,
-                cell_volume.data,
+                self._get_floating_point(cell_volume),
                 multiplicity.data,
                 dm_total.data,
                 signed_water_mass.data,
diff --git a/PySDM/dynamics/isotopic_fractionation.py b/PySDM/dynamics/isotopic_fractionation.py
index a27580d0b4..0767525408 100644
--- a/PySDM/dynamics/isotopic_fractionation.py
+++ b/PySDM/dynamics/isotopic_fractionation.py
@@ -41,8 +41,10 @@ def register(self, builder):
                 raise AssertionError(
                     f"Isotopic fractionation not implemented for {isotope}"
                 )
-            builder.request_attribute(f"moles_{isotope}")
             builder.request_attribute(f"Bolin number for {isotope}")
 
+        for isotope in HEAVY_ISOTOPES:
+            builder.request_attribute(f"moles_{isotope}")
+
     def __call__(self):
         self.particulator.isotopic_fractionation(self.isotopes)
diff --git a/PySDM/particulator.py b/PySDM/particulator.py
index 236e5dc7a6..81ffa75cd1 100644
--- a/PySDM/particulator.py
+++ b/PySDM/particulator.py
@@ -456,7 +456,7 @@ def isotopic_fractionation(self, heavy_isotopes: tuple):
                 multiplicity=self.attributes["multiplicity"],
                 dm_total=self.attributes["diffusional growth mass change"],
                 signed_water_mass=self.attributes["signed water mass"],
-                dry_air_density=self.environment["dry_air_density"],
+                dry_air_density=self.environment["rhod"],
                 molar_mass_heavy_molecule=getattr(
                     self.formulae.constants,
                     {
diff --git a/PySDM/physics/isotope_diffusivity_ratios/grahams_law.py b/PySDM/physics/isotope_diffusivity_ratios/grahams_law.py
index 7079a1848f..b380781fae 100644
--- a/PySDM/physics/isotope_diffusivity_ratios/grahams_law.py
+++ b/PySDM/physics/isotope_diffusivity_ratios/grahams_law.py
@@ -18,3 +18,15 @@ def ratio_3H_heavy_to_light(const, temperature):  # pylint: disable=unused-argum
         return (
             (2 * const.M_1H + const.M_16O) / (const.M_3H + const.M_1H + const.M_16O)
         ) ** const.ONE_HALF
+
+    @staticmethod
+    def ratio_17O_heavy_to_light(const, temperature):  # pylint: disable=unused-argument
+        return (
+            (2 * const.M_1H + const.M_16O) / (2 * const.M_1H + const.M_17O)
+        ) ** const.ONE_HALF
+
+    @staticmethod
+    def ratio_18O_heavy_to_light(const, temperature):  # pylint: disable=unused-argument
+        return (
+            (2 * const.M_1H + const.M_16O) / (2 * const.M_1H + const.M_18O)
+        ) ** const.ONE_HALF
diff --git a/PySDM/physics/isotope_ratio_evolution/__init__.py b/PySDM/physics/isotope_ratio_evolution/__init__.py
index 44b0ec42f3..fd021d375b 100644
--- a/PySDM/physics/isotope_ratio_evolution/__init__.py
+++ b/PySDM/physics/isotope_ratio_evolution/__init__.py
@@ -4,3 +4,4 @@
 from .merlivat_and_jouzel_1979 import MerlivatAndJouzel1979
 from .rayleigh_distillation import RayleighDistillation
 from .gedzelman_and_arnold_1994 import GedzelmanAndArnold1994
+from .zaba_et_al import ZabaEtAl
diff --git a/PySDM/physics/isotope_ratio_evolution/zaba_et_al.py b/PySDM/physics/isotope_ratio_evolution/zaba_et_al.py
new file mode 100644
index 0000000000..381cf2232f
--- /dev/null
+++ b/PySDM/physics/isotope_ratio_evolution/zaba_et_al.py
@@ -0,0 +1,25 @@
+"""
+saturation when no change in isotopic ratio
+in liquid and in vapour
+"""
+
+
+# pylint: disable=too-few-public-methods
+class ZabaEtAl:
+    def __init__(self, _):
+        pass
+
+    # pylint: disable=too-many-arguments
+    @staticmethod
+    def saturation_for_zero_dR_condition(
+        _,
+        diff_rat_heavy_to_light,
+        iso_ratio_x,
+        iso_ratio_r,
+        iso_ratio_v,
+        alpha,
+        Fd,
+        Fk,
+    ):
+        A = alpha / diff_rat_heavy_to_light * iso_ratio_x / iso_ratio_r - 1
+        return A / ((1 + Fk / Fd) * (1 - alpha * iso_ratio_v / iso_ratio_r) + A)
diff --git a/PySDM/physics/isotope_relaxation_timescale/zaba_et_al.py b/PySDM/physics/isotope_relaxation_timescale/zaba_et_al.py
index 21770e9587..d75311eef0 100644
--- a/PySDM/physics/isotope_relaxation_timescale/zaba_et_al.py
+++ b/PySDM/physics/isotope_relaxation_timescale/zaba_et_al.py
@@ -22,15 +22,13 @@ def tau(
 
     @staticmethod
     def bolin_number(
-        const,
+        relative_humidity,
         D_ratio_heavy_to_light,
         alpha,
-        D_light,
-        Fk,
         R_vap,
         R_liq,
-        relative_humidity,
-        rho_v,
+        Fd,
+        Fk,
     ):  # pylint: disable=too-many-arguments,too-many-positional-arguments
         # TODO #1809 Numba can't compile when * in bolin_number
         """Heavy to total isotopic-timescales ratio (tau_heavy/tau_total).
@@ -43,9 +41,7 @@ def bolin_number(
             * (1 - relative_humidity)
             / D_ratio_heavy_to_light
             / (
-                (1 + rho_v * D_light * Fk / const.rho_w)
-                * relative_humidity
-                * (1 - alpha * R_vap / R_liq)
+                (1 + Fk / Fd) * relative_humidity * (1 - alpha * R_vap / R_liq)
                 - relative_humidity
                 + 1
             )
diff --git a/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb b/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb
index 6f54294838..d384386d11 100644
--- a/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb
+++ b/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb
@@ -29,9 +29,7 @@
     "https://doi.org/10.1029/93JD03518\n",
     "\n",
     "**Description**  \n",
-    "Theoretical curves from the paper are reproduced as in Fig. 2, separately for liquid and vapour isotopic content (black lines in Figure 1 in this notebook).\n",
-    "The figure illustrates the change in Deuterium isotopic composition in both liquid and vapour phases following an arbitrary water mass change, relative to the theoretical lines.\n",
-    "Figure 1 was submitted for 2026 AMS Madison Summit (17th Conference on Cloud Physics).\n",
+    "Theoretical curves from the paper are reproduced as in Fig. 2, separately for liquid and vapour isotopic content. The figure illustrates the change in Deuterium isotopic composition in both liquid and vapour phases following an arbitrary water mass change, relative to the theoretical lines.\n",
     "\n",
     "**Notes**  \n",
     "- Parameter *b* in Eq. (18) of the paper appears to be missing a multiplication by $\\rho_\\text{s}$. Including $\\rho_\\text{s}$ corrects the units and aligns the reproduced plot with expectations. This suggests a likely typographical error in the original equation.\n",
@@ -49,12 +47,7 @@
   {
    "cell_type": "code",
    "id": "2088324ebcd34d62",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-17T20:13:26.803639Z",
-     "start_time": "2026-04-17T20:13:26.798354Z"
-    }
-   },
+   "metadata": {},
    "source": [
     "import os, sys\n",
     "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue'  # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n",
@@ -69,21 +62,12 @@
   {
    "cell_type": "code",
    "id": "50b690c4d6b87266",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-04-17T20:13:29.707976Z",
-     "start_time": "2026-04-17T20:13:26.815111Z"
-    }
-   },
+   "metadata": {},
    "source": [
-    "# pylint: disable=redefined-outer-name\n",
-    "\n",
-    "from dataclasses import dataclass\n",
     "from collections import namedtuple\n",
     "import numpy as np\n",
     "from matplotlib import pyplot\n",
     "from matplotlib.lines import Line2D\n",
-    "\n",
     "from open_atmos_jupyter_utils import show_plot\n",
     "\n",
     "from PySDM import Formulae\n",
@@ -125,8 +109,8 @@
    "id": "cb7fd1c2a10c76c5",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:13:29.742776Z",
-     "start_time": "2026-04-17T20:13:29.712137Z"
+     "end_time": "2026-05-14T09:15:39.038741Z",
+     "start_time": "2026-05-14T09:15:39.018321Z"
     }
    },
    "source": [
@@ -176,8 +160,8 @@
    "id": "c8f7e3e23a33b364",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:13:29.760101Z",
-     "start_time": "2026-04-17T20:13:29.749307Z"
+     "end_time": "2026-05-14T09:15:39.049348Z",
+     "start_time": "2026-05-14T09:15:39.042588Z"
     }
    },
    "source": [
@@ -192,10 +176,9 @@
     "        \"vsmow\",\n",
     "    ]\n",
     ")\n",
-    "\n",
     "Commons = namedtuple(\"Commons\", [\"f\", \"c\", \"iso\", \"T\", \"params\"])\n",
     "\n",
-    "class CommonsFactory:\n",
+    "class CommonsFactory: #pylint: disable=too-few-public-methods\n",
     "    \"\"\"Compute and assemble common isotope quantities.\"\"\"\n",
     "    def __init__(self, formulae):\n",
     "        self._f = formulae\n",
@@ -212,6 +195,7 @@
     "        )\n",
     "\n",
     "    def _build_params(self, ctx):\n",
+    "        \"\"\"Calculate parameters from formulae\"\"\"\n",
     "        vsmow = getattr(self._const, f\"VSMOW_R_{ctx.iso}\")\n",
     "        diff_fun = getattr(\n",
     "            self._f.isotope_diffusivity_ratios,\n",
@@ -225,12 +209,12 @@
     "            )\n",
     "        )\n",
     "        pvs = self._f.saturation_vapour_pressure.pvs_water(ctx.T)\n",
-    "        Fk = self._f.drop_growth.Fk(\n",
+    "        fk = self._f.drop_growth.Fk(\n",
     "            T=ctx.T,\n",
     "            K=self._const.K0,\n",
     "            lv=self._f.latent_heat_vapourisation.lv(ctx.T),\n",
     "        )\n",
-    "        Fd = self._f.drop_growth.Fd(\n",
+    "        fd = self._f.drop_growth.Fd(\n",
     "            pvs=pvs,\n",
     "            T=ctx.T,\n",
     "            D=self._const.D0,\n",
@@ -239,13 +223,13 @@
     "        return Parameters(\n",
     "            D_ratio=D_ratio,\n",
     "            f_ratio=ventilation_ratio,\n",
-    "            alpha_w=self.alpha_w(ctx),\n",
-    "            Fk_plus_Fd=Fk + Fd,\n",
-    "            b=rho_s * Fk / self._const.rho_w * self._const.D0,\n",
+    "            alpha_w=self._alpha_w(ctx),\n",
+    "            Fk_plus_Fd=fk + fd,\n",
+    "            b=rho_s * fk / self._const.rho_w * self._const.D0,\n",
     "            vsmow=vsmow,\n",
     "        )\n",
     "    \n",
-    "    def alpha_w(self, ctx: IsotopeContext) -> float:\n",
+    "    def _alpha_w(self, ctx: IsotopeContext) -> float:\n",
     "        \"\"\"Calculate isotopic fractionation factor for specified isotope\"\"\"\n",
     "        alpha_fun = getattr(\n",
     "            self._f.isotope_equilibrium_fractionation_factors,\n",
@@ -260,8 +244,7 @@
     "            return alpha_fun(np.nan, alpha_l_18o)\n",
     "        return alpha_fun(ctx.T)\n",
     "    \n",
-    "C2K = Formulae().trivia.C2K\n",
-    "K2C = Formulae().trivia.K2C"
+    "C2K = Formulae().trivia.C2K"
    ],
    "outputs": [],
    "execution_count": 4
@@ -269,23 +252,27 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:13:29.772306Z",
-     "start_time": "2026-04-17T20:13:29.769495Z"
+     "end_time": "2026-05-14T09:15:39.054359Z",
+     "start_time": "2026-05-14T09:15:39.052628Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "def no_fractionation_saturation(x, iso_ratio_v, cmn, phase):\n",
-    "    iso_ratio_r = x * cmn.params.vsmow\n",
-    "    return cmn.f.isotope_ratio_evolution.saturation_for_zero_dR_condition(\n",
-    "        iso_ratio_x=iso_ratio_r if phase=='liquid' else iso_ratio_v,\n",
+    "def no_fractionation_saturation(iso_ratio_over_vsmow, iso_ratio_vap, commons, water_phase):\n",
+    "    \"\"\"\n",
+    "    Wrapper for saturation_for_zero_dR_condition formula\n",
+    "    utilising precomputed parameters from commons\n",
+    "    \"\"\"\n",
+    "    iso_ratio_liq = iso_ratio_over_vsmow * commons.params.vsmow\n",
+    "    return commons.f.isotope_ratio_evolution.saturation_for_zero_dR_condition(\n",
+    "        iso_ratio_x=iso_ratio_liq if water_phase=='liquid' else iso_ratio_vap,\n",
     "        diff_rat_light_to_heavy=(\n",
-    "            cmn.params.f_ratio / cmn.params.D_ratio\n",
+    "            commons.params.f_ratio / commons.params.D_ratio\n",
     "        ),\n",
-    "        b=cmn.params.b,\n",
-    "        alpha_w=cmn.params.alpha_w,\n",
-    "        iso_ratio_r=iso_ratio_r,\n",
-    "        iso_ratio_v=iso_ratio_v,\n",
+    "        b=commons.params.b,\n",
+    "        alpha_w=commons.params.alpha_w,\n",
+    "        iso_ratio_r=iso_ratio_liq,\n",
+    "        iso_ratio_v=iso_ratio_vap,\n",
     "    )"
    ],
    "id": "134c3082c2f45ee1",
@@ -295,15 +282,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:34:54.750883Z",
-     "start_time": "2026-04-17T20:34:54.379790Z"
+     "end_time": "2026-05-14T09:15:39.366926Z",
+     "start_time": "2026-05-14T09:15:39.056426Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "@dataclass(frozen=True)\n",
-    "class Paper:\n",
-    "    \"\"\"Configuration reproducing the paper setup.\"\"\"\n",
+    "class Paper: #pylint: disable=too-few-public-methods\n",
+    "    \"\"\"Configuration reproducing the paper setup. Instance is not needed.\"\"\"\n",
     "    formulae = Formulae(\n",
     "        isotope_equilibrium_fractionation_factors=\"MerlivatAndNief1967+Majoube1971\",\n",
     "        isotope_kinetic_fractionation_factors=\"JouzelAndMerlivat1984\",\n",
@@ -322,13 +308,13 @@
    ],
    "id": "84ac9d90edbfffa6",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:34:56.826201Z",
-     "start_time": "2026-04-17T20:34:54.763002Z"
+     "end_time": "2026-05-14T09:15:41.932496Z",
+     "start_time": "2026-05-14T09:15:39.370487Z"
     }
    },
    "cell_type": "code",
@@ -336,38 +322,53 @@
     "cmn = CommonsFactory(Paper.formulae).build(\n",
     "    IsotopeContext(iso=Paper.isotope, T=Paper.temperature)\n",
     ")\n",
-    "RH, R_liq = np.meshgrid(\n",
-    "    np.linspace(0.01, 1.1, 32),\n",
-    "    np.linspace(*Paper.xlim, 32) * cmn.params.vsmow\n",
-    ")\n",
-    "f = cmn.f\n",
+    "CMN_FOR_TEST = cmn"
+   ],
+   "id": "b3ae06a4bb0527ea",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-05-14T09:15:42.103790Z",
+     "start_time": "2026-05-14T09:15:41.936722Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "RH = np.linspace(0.01, 1.1, 32)\n",
+    "R_liq_over_vsmow = np.linspace(*Paper.xlim, 32)\n",
+    "\n",
+    "XX, YY = np.meshgrid(R_liq_over_vsmow, RH)\n",
+    "\n",
     "T = cmn.T\n",
     "rho_v = cmn.f.saturation_vapour_pressure.pvs_water(cmn.T) / cmn.T / cmn.c.Rv\n",
     "Fk = cmn.f.drop_growth.Fk(T=cmn.T, K=cmn.c.K0, lv=cmn.c.l_tri)\n",
+    "Fd = cmn.f.drop_growth.Fd(T=cmn.T, D=cmn.c.D0, pvs=cmn.f.saturation_vapour_pressure.pvs_water(cmn.T))\n",
+    "\n",
     "ISO_RATIO_V = cmn.f.trivia.isotopic_delta_2_ratio(Paper.delta_v, cmn.params.vsmow)\n",
-    "X_eq = ISO_RATIO_V * cmn.params.alpha_w /cmn.params.vsmow\n",
+    "X_eq = ISO_RATIO_V * cmn.params.alpha_w / cmn.params.vsmow\n",
     "Y_eq = in_unit(1, Paper.rh_unit)\n",
-    "\n",
     "Bolin_number = cmn.f.isotope_relaxation_timescale.bolin_number(\n",
     "    D_ratio_heavy_to_light=cmn.params.D_ratio,\n",
     "    alpha=cmn.params.alpha_w,\n",
-    "    D_light=cmn.c.D0,\n",
+    "    Fd=Fd,\n",
     "    Fk=Fk,\n",
     "    R_vap=ISO_RATIO_V,\n",
-    "    R_liq=R_liq,\n",
-    "    relative_humidity=RH,\n",
-    "    rho_v=rho_v,\n",
-    ").T"
+    "    R_liq=XX * cmn.params.vsmow,\n",
+    "    relative_humidity=YY,\n",
+    ")"
    ],
    "id": "2a5f9b367fe6e519",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 8
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:34:56.857253Z",
-     "start_time": "2026-04-17T20:34:56.854076Z"
+     "end_time": "2026-05-14T09:15:42.108805Z",
+     "start_time": "2026-05-14T09:15:42.106905Z"
     }
    },
    "cell_type": "code",
@@ -380,23 +381,24 @@
     "        \"linestyle\": \"-\",\n",
     "    },\n",
     "    \"vapour\": {\n",
-    "        \"eqs\":  R_liq.T / ISO_RATIO_V - Bolin_number,\n",
+    "        \"eqs\":  XX * cmn.params.vsmow / ISO_RATIO_V - Bolin_number,\n",
     "        \"label\": \"$\\\\bf{{(B)}}$ \"\n",
     "                 + \"$\\\\frac{\\\\text{R}_\\\\text{liq}}{\\\\text{R}_\\\\text{vap}} - \\\\text{Bo}$ [1]\",\n",
     "        \"center\": 0,\n",
     "        \"linestyle\": \"--\",\n",
     "    }\n",
-    "}"
+    "}\n",
+    "plots = {\"liquid\": {}, \"vapour\": {}}"
    ],
    "id": "c08afc2d451f991f",
    "outputs": [],
-   "execution_count": 13
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:35:01.391423Z",
-     "start_time": "2026-04-17T20:34:59.049604Z"
+     "end_time": "2026-05-14T09:15:43.121393Z",
+     "start_time": "2026-05-14T09:15:42.111312Z"
     }
    },
    "cell_type": "code",
@@ -404,27 +406,28 @@
     "fig, axs = pyplot.subplots(\n",
     "    1, 2,\n",
     "    figsize=(10,4),\n",
-    "    constrained_layout=True,\n",
     ")\n",
-    "PLOT_LINE = {'liquid': None, 'vapour': None}\n",
-    "PCM = {'liquid': None, 'vapour': None}\n",
-    "\n",
-    "X, Y = R_liq[:, 0] / cmn.params.vsmow, in_unit(RH[0, :], Paper.rh_unit)\n",
     "v = .2\n",
-    "for i, c in enumerate(cases.items()):\n",
-    "    axs[i].set_title(f\"{c[1]['label']}\\n\")\n",
-    "    pcm = axs[i].pcolormesh(X, Y, c[1][\"eqs\"],\n",
-    "                cmap='RdBu_r', vmin=-v + c[1]['center'], vmax=v + c[1]['center'])\n",
-    "    cb = fig.colorbar(pcm, ax=axs[i])\n",
-    "    cb.ax.set_title('[1]')\n",
-    "    PCM[c[0]] = pcm\n",
-    "    for phase in cases:\n",
-    "        x = np.linspace(X_eq, 1.1, 500)\n",
-    "        y = no_fractionation_saturation(x, ISO_RATIO_V, cmn, phase)\n",
-    "        ls = \"-\" if phase == c[0] else \":\"\n",
-    "        PLOT_LINE[phase] = axs[i].plot(x, y / PER_CENT, c='k', label=rf\"$S_\\text{{{phase}}}^\\text{{eq}}$\", ls=ls, lw=2)\n",
-    "            \n",
-    "    axs[i].plot(X, np.full_like(X, Y_eq), c='r', label='phase equilibrium')\n",
+    "for i, (phase, params) in enumerate(cases.items()):\n",
+    "    pcm = axs[i].pcolormesh(R_liq_over_vsmow, in_unit(RH, Paper.rh_unit), params[\"eqs\"],\n",
+    "                cmap='RdBu_r', vmin=-v + params['center'], vmax=v + params['center'])\n",
+    "    cases[phase][\"pcolormesh\"] = pcm\n",
+    "    cb = fig.colorbar(pcm, ax=axs[i], orientation='horizontal', location='top', aspect=30, fraction=0.05)\n",
+    "    cb.set_label(f\"{params['label']}\\n\", fontsize=12)\n",
+    "    \n",
+    "    for p in cases:\n",
+    "        x = np.linspace(0, 1.05, 2000)\n",
+    "        y = no_fractionation_saturation(x, ISO_RATIO_V, cmn, p)\n",
+    "        plots[p][\"x\"] = x\n",
+    "        plots[p][\"y\"] = y\n",
+    "        y_masked = np.where((0.0 < y) & (y < 1.05), y / PER_CENT, np.nan)\n",
+    "        axs[i].plot(\n",
+    "            x, y_masked,\n",
+    "            label=rf\"$S_\\text{{{p}}}^\\text{{eq}}$\",\n",
+    "            ls=\"-\" if p[0] == 'l' else \":\", lw=2, c='k',\n",
+    "        )\n",
+    "    \n",
+    "    axs[i].plot(R_liq_over_vsmow, np.full_like(R_liq_over_vsmow, Y_eq), c='r', label='phase eq')\n",
     "    axs[i].annotate('phase & isotopic\\nequilibria', xy=(X_eq, Y_eq), color='white',\n",
     "        arrowprops={\"arrowstyle\": '->', \"color\": 'white', \"lw\": 1},\n",
     "                    textcoords='offset points',xytext=(40, -25),)\n",
@@ -432,7 +435,7 @@
     "    \n",
     "for ax in axs:\n",
     "    ax.set_xlabel(\"R$_\\\\text{liq}$ / R$_\\\\text{SMOW}$ [1]\")\n",
-    "    ax.set_xlim(X[0], X[-1])\n",
+    "    ax.set_xlim(R_liq_over_vsmow[0], R_liq_over_vsmow[-1])\n",
     "    ax.set_ylabel(\"S=RH [%]\")\n",
     "    ax.set_ylim(0, 105)\n",
     "    ax.legend(loc=\"lower right\")\n",
@@ -442,22 +445,25 @@
     "the following condition must be met: $\\left(\\frac{{R_{{\\text{{liq}}}}}}{{R_{{\\text{{vap}}}}}} - \\text{{Bo}} = 0\\right)$. The isotopologue considered in both plots is $^1\\mathrm{{H}}^2\\mathrm{{H}}^{{16}}\\mathrm{{O}}$.\n",
     "Black lines: isotopic equilibrium curves from Gedzelman & Arnold (1994, Fig. 2);\n",
     "red line: phase equilibrium (RH = 100%); white dot: phase and isotopic equilibrium, $R_{{\\text{{liq}}}} = \\alpha(T)\\,R_{{\\text{{vap}}}}$.\n",
-    "The environmental setting follows that of the paper: $T = {K2C(Paper.temperature):.0f}^\\circ\\mathrm{{C}}$, $\\delta_{{^2\\mathrm{{H}}}} = \\frac{{R_{{\\text{{vap}}}}}}{{R_{{\\text{{SMOW}}}}}} - 1 = {in_unit(Paper.delta_v, PER_MILLE):.0f}\\,‰$.\n",
+    "The environmental setting follows that of the paper: $T = {Paper.temperature-273.15:.0f}^\\circ\\mathrm{{C}}$, $\\delta_{{^2\\mathrm{{H}}}} = \\frac{{R_{{\\text{{vap}}}}}}{{R_{{\\text{{SMOW}}}}}} - 1 = {in_unit(Paper.delta_v, PER_MILLE):.0f}\\,‰$.\n",
     "\"\"\"\n",
     "fig.text(0.5, -0.5, text, ha=\"center\", fontsize=12)\n",
-    "show_plot(\"Bo\")"
+    "show_plot(\"Bo\", inline_format='png')"
    ],
-   "id": "25685d6ad0b6e1df",
+   "id": "800c6c40056f462e",
    "outputs": [
     {
      "data": {
       "text/plain": [
        "<Figure size 1000x400 with 4 Axes>"
       ],
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"
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
@@ -467,41 +473,43 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "cdb721f0a9f6431faebaac659c56aca8"
+       "model_id": "3354241dd1e84df1823e4a19b1cf9cf4"
       }
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "execution_count": 14
+   "execution_count": 10
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Extensions",
-   "id": "699cbeeb8afafbba"
+   "id": "895f732d8e02384d"
   },
   {
-   "cell_type": "code",
-   "id": "cd19a4efb40c3b22",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:13:40.413436Z",
-     "start_time": "2026-04-17T20:13:36.675594Z"
+     "end_time": "2026-05-14T09:15:45.982674Z",
+     "start_time": "2026-05-14T09:15:43.131513Z"
     }
    },
+   "cell_type": "code",
    "source": [
-    "formulae = Formulae(\n",
+    "f = Formulae(\n",
     "    isotope_equilibrium_fractionation_factors=\"BarkanAndLuz2005+HoritaAndWesolowski1994\",\n",
     "    isotope_diffusivity_ratios=\"HellmannAndHarvey2020\",\n",
     "    isotope_meteoric_water_line=\"BarkanAndLuz2007+Dansgaard1964\",\n",
     "    isotope_ratio_evolution=\"GedzelmanAndArnold1994\",\n",
     "    isotope_ventilation_ratio=\"Neglect\",\n",
     ")\n",
-    "commons_factory = CommonsFactory(formulae)\n",
+    "commons_factory = CommonsFactory(f)\n",
     "\n",
-    "iso_mwl = formulae.isotope_meteoric_water_line\n",
+    "iso_mwl = f.isotope_meteoric_water_line\n",
     "d2H = (-300, -200, -100)\n",
     "deltas_2H = [x * PER_MILLE for x in d2H]\n",
     "deltas_18O = [iso_mwl.d18O_of_d2H(d) for d in deltas_2H]\n",
@@ -515,26 +523,27 @@
     "    for j_delta, delta in enumerate((deltas_2H, deltas_18O, deltas_17O)[idx_iso]):\n",
     "        ax = axs[idx_iso][j_delta]\n",
     "        for t in Ts:\n",
-    "            commons = commons_factory.build(IsotopeContext(iso, t))\n",
-    "            iso_ratio_v = commons.f.trivia.isotopic_delta_2_ratio(delta, commons.params.vsmow)\n",
+    "            cmn = commons_factory.build(IsotopeContext(iso, t))\n",
+    "            iso_ratio_v = cmn.f.trivia.isotopic_delta_2_ratio(delta, cmn.params.vsmow)\n",
     "            x = np.linspace(\n",
-    "                commons.params.alpha_w * iso_ratio_v / commons.params.vsmow, 1.1, 200\n",
+    "                cmn.params.alpha_w * iso_ratio_v  / cmn.params.vsmow, 1.1, 200\n",
     "            )\n",
-    "            y_liq = no_fractionation_saturation(x, iso_ratio_v, commons, 'liquid')\n",
-    "            y_vap = no_fractionation_saturation(x, iso_ratio_v, commons, 'vapour')\n",
+    "            y_liq = no_fractionation_saturation(x, iso_ratio_v, cmn, 'liquid')\n",
+    "            y_vap = no_fractionation_saturation(x, iso_ratio_v, cmn, 'vapour')\n",
     "\n",
-    "            color = (\n",
+    "            c = (\n",
     "                (t - Ts[0]) / (Ts[-1] - Ts[0]),\n",
     "                0.333,\n",
     "                1 - (t - Ts[0]) / (Ts[-1] - Ts[0]),\n",
     "            )\n",
     "            if t in (Ts[0], Ts[-1]):\n",
     "                ax.annotate(\n",
-    "                    f\" {K2C(t):g}°C\", (x_lim[iso][1], y_liq[-1]), fontsize=8, ha='left', va='center'\n",
+    "                    f\"{Formulae().trivia.K2C(t):g}°C\",\n",
+    "                    (x_lim[iso][1], y_liq[-1] - 0.015),\n",
+    "                    fontsize=8\n",
     "                )\n",
-    "\n",
-    "            ax.plot(x, y_liq, color=color, linestyle=\"-\")\n",
-    "            ax.plot(x, y_vap, color=color, linestyle=\":\")\n",
+    "            ax.plot(x, y_liq, c=c, ls=\"-\")\n",
+    "            ax.plot(x, y_vap, c=c, ls=\":\")\n",
     "\n",
     "        ax.set(\n",
     "            title=f\"ambient vapour $\\\\delta^{{{iso[:-1]}}}{iso[-1]}$ = {in_unit(delta, PER_MILLE):.3g}‰\",\n",
@@ -561,16 +570,20 @@
     "fig.subplots_adjust(top=0.88, bottom=0)\n",
     "show_plot(\"plot_grid.pdf\")"
    ],
+   "id": "5f52ac488de12c95",
    "outputs": [
     {
      "data": {
       "text/plain": [
        "<Figure size 1000x800 with 9 Axes>"
       ],
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      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
@@ -580,25 +593,28 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "87967ac5a0f34639b76ff61653214427"
+       "model_id": "91b1bc6f151d47fda6517d2ce956ed5c"
       }
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "execution_count": 10
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-04-17T20:13:40.543694Z",
-     "start_time": "2026-04-17T20:13:40.532349Z"
+     "end_time": "2026-05-14T09:15:46.001406Z",
+     "start_time": "2026-05-14T09:15:45.999978Z"
     }
    },
    "cell_type": "code",
    "source": "",
-   "id": "2d3e41abde5dba54",
+   "id": "a6c0fffbdc6f74e9",
    "outputs": [],
    "execution_count": null
   }
diff --git a/tests/smoke_tests/no_env/gedzelman_and_arnold_1994/test_fig_2.py b/tests/smoke_tests/no_env/gedzelman_and_arnold_1994/test_fig_2.py
index e7a32c479a..9704119461 100644
--- a/tests/smoke_tests/no_env/gedzelman_and_arnold_1994/test_fig_2.py
+++ b/tests/smoke_tests/no_env/gedzelman_and_arnold_1994/test_fig_2.py
@@ -10,8 +10,6 @@
 from open_atmos_jupyter_utils import notebook_vars
 from PySDM_examples import Gedzelman_and_Arnold_1994
 
-from PySDM.physics.constants import PER_CENT
-
 PLOT = False
 
 
@@ -43,8 +41,8 @@ def test_fig_2(notebook_variables, x, expected_y, phase):
     """
 
     # arrange
-    xy_data = notebook_variables["PLOT_LINE"][phase][0].get_xydata()
-    plot_x, plot_y = xy_data[:, 0], xy_data[:, 1] * PER_CENT
+    plot_x = notebook_variables["plots"][phase]["x"]
+    plot_y = notebook_variables["plots"][phase]["y"]
     plot_x_eps = (plot_x[1] - plot_x[0]) / 2
     plot_y_eps = np.max(abs(np.diff(plot_y))) / 2
 
@@ -56,37 +54,37 @@ def test_fig_2(notebook_variables, x, expected_y, phase):
     np.testing.assert_allclose(
         actual=sut,
         desired=expected_y,
-        atol=plot_y_eps / plot_x_eps,
+        rtol=plot_y_eps,
     )
 
 
 @pytest.mark.parametrize(
-    "phase, condition, atol",
+    "phase, condition, rtol, eps",
     (
-        ("vapour", 0, 0.2),
-        ("liquid", 1, 0.02),
+        ("vapour", 0.0, 0.1, 1e-3),
+        ("liquid", 1.0, 0.1, 1e-2),
     ),
 )
-def test_dR_zero_condition(notebook_variables, phase, condition, atol):
+def test_dR_zero_condition(notebook_variables, phase, condition, rtol, eps):
     """Test values plotted with color in Fig 1.
     Points (x, y) for which z equals condition should match theoretical lines."""
     # arrange
-    cmn = notebook_variables["cmn"]
-
-    X_eq = notebook_variables["X_eq"]
+    cmn = notebook_variables["CMN_FOR_TEST"]
     iso_ratio_v = notebook_variables["ISO_RATIO_V"]
 
-    X, Y = np.meshgrid(notebook_variables["X"], notebook_variables["Y"] * PER_CENT)
-    pcm_data = notebook_variables["PCM"][phase].get_array()
+    Y = notebook_variables["YY"]
+    X = notebook_variables["XX"]
 
-    # act
+    pcm_data = notebook_variables["cases"][phase]["pcolormesh"].get_array()
     within = (
-        (condition - 0.005 < pcm_data) & (pcm_data < condition + 0.005) & (X >= X_eq)
+        (condition - eps < pcm_data)
+        & (pcm_data < condition + eps)
+        & (X > notebook_variables["X_eq"])
     )
+    assert np.sum(within) > 0
 
-    x_to_check = X[within]
-    y_to_check = Y[within]
-    iso_ratio_r = x_to_check * cmn.params.vsmow
+    # act
+    iso_ratio_r = X[within] * cmn.params.vsmow
     expected_y = cmn.f.isotope_ratio_evolution.saturation_for_zero_dR_condition(
         iso_ratio_x=iso_ratio_r if phase == "liquid" else iso_ratio_v,
         diff_rat_light_to_heavy=(cmn.params.f_ratio / cmn.params.D_ratio),
@@ -97,5 +95,4 @@ def test_dR_zero_condition(notebook_variables, phase, condition, atol):
     )
 
     # assert
-    assert np.sum(within) > 0
-    np.testing.assert_allclose(y_to_check, expected_y, atol=atol)
+    np.testing.assert_allclose(Y[within], expected_y, rtol=rtol)
diff --git a/tests/unit_tests/attributes/test_isotopes.py b/tests/unit_tests/attributes/test_isotopes.py
index 70fe03b69a..954bca5a9e 100644
--- a/tests/unit_tests/attributes/test_isotopes.py
+++ b/tests/unit_tests/attributes/test_isotopes.py
@@ -134,7 +134,7 @@ def test_bolin_number_attribute(
         any_positive_number = 44.0
         ff = Formulae(
             isotope_relaxation_timescale=variant,
-            isotope_diffusivity_ratios="HellmannAndHarvey2020",
+            isotope_diffusivity_ratios="HellmannAndHarvey2020+GrahamsLaw",
             isotope_equilibrium_fractionation_factors="VanHook1968",
         )
         n_sd = 1
@@ -165,7 +165,7 @@ def test_bolin_number_attribute(
         particulator = builder.build(attributes=attr)
         particulator.environment["RH"] = relative_humidity
         particulator.environment["T"] = any_positive_number
-        particulator.environment["dry_air_density"] = any_positive_number
+        particulator.environment["rhod"] = any_positive_number
 
         # act
         value = particulator.attributes[attribute_name].data
diff --git a/tests/unit_tests/backends/test_isotope_methods.py b/tests/unit_tests/backends/test_isotope_methods.py
index ea15203049..e53d55f7b5 100644
--- a/tests/unit_tests/backends/test_isotope_methods.py
+++ b/tests/unit_tests/backends/test_isotope_methods.py
@@ -3,6 +3,9 @@
 """
 
 import numpy as np
+import pytest
+
+from PySDM import Formulae
 
 
 class TestIsotopeMethods:
@@ -25,7 +28,7 @@ def test_isotopic_fractionation(backend_instance):
         # act
         backend.isotopic_fractionation(
             cell_id=arr2storage(np.array([0], dtype=int)),
-            cell_volume=arr2storage(np.array([2.0])),
+            cell_volume=2.0,
             multiplicity=arr2storage(np.array([3.0])),
             dm_total=arr2storage(np.array([0.2])),
             signed_water_mass=arr2storage(np.array([1.0])),
@@ -54,3 +57,52 @@ def test_isotopic_delta(backend_instance):
 
         # assert
         assert (output.to_ndarray() == -1).all()
+
+    @staticmethod
+    def test_bolin_number(backend_class):
+        # arrange
+        if backend_class.__name__ == "ThrustRTC":
+            pytest.xfail("bolin_number not yet supported for ThrustRTC")
+
+        backend = backend_class(
+            Formulae(
+                isotope_diffusivity_ratios="Stewart1975+GrahamsLaw",
+                isotope_relaxation_timescale="ZabaEtAl",
+                isotope_equilibrium_fractionation_factors="VanHook1968",
+            )
+        )
+
+        arr2storage = backend.Storage.from_ndarray
+
+        n_sd = 3
+        n_cell = 2
+
+        output = arr2storage(np.empty(n_sd))
+
+        cell_id = arr2storage(np.zeros(n_sd, dtype=np.int64))
+        relative_humidity = arr2storage(np.full(n_cell, 0.95))
+        temperature = arr2storage(np.full(n_cell, 283.15))
+        density_dry_air = arr2storage(np.full(n_cell, 1.2))
+        moles_light_molecule = arr2storage(np.array([1e-3, 2e-3, 3e-3]))
+        moles_heavy = arr2storage(np.array([2e-6, 4e-6, 6e-6]))
+        molality_in_dry_air = arr2storage(np.full(n_cell, 1e-5))
+
+        # act
+        backend.bolin_number(
+            output=output,
+            cell_id=cell_id,
+            isotope="2H",
+            relative_humidity=relative_humidity,
+            temperature=temperature,
+            density_dry_air=density_dry_air,
+            moles_light_molecule=moles_light_molecule,
+            moles_heavy=moles_heavy,
+            molality_in_dry_air=molality_in_dry_air,
+        )
+
+        # assert
+        result = output.to_ndarray()
+
+        assert result.shape == (n_sd,)
+        assert np.all(np.isfinite(result))
+        assert np.all(result > 0)
diff --git a/tests/unit_tests/dynamics/test_isotopic_fractionation.py b/tests/unit_tests/dynamics/test_isotopic_fractionation.py
index 0f78d38362..fe2ed66cb7 100644
--- a/tests/unit_tests/dynamics/test_isotopic_fractionation.py
+++ b/tests/unit_tests/dynamics/test_isotopic_fractionation.py
@@ -34,7 +34,7 @@ def make_particulator(
     builder = Builder(
         n_sd=1,
         backend=backend_instance,
-        environment=Box(dv=np.ones(1), dt=1 * si.s),
+        environment=Box(dv=np.nan, dt=1 * si.s),
     )
     for iso in isotopes_considered:
         if not attributes.get(f"moles_{iso}"):
@@ -43,7 +43,7 @@ def make_particulator(
         builder.request_attribute(f"delta_{iso}")
     builder.particulator.environment["RH"] = np.array(rh)
     builder.particulator.environment["T"] = np.array(t)
-    builder.particulator.environment["dry_air_density"] = np.array(1)
+    builder.particulator.environment["rhod"] = np.array(1)
     builder.add_dynamic(Condensation())
     builder.add_dynamic(IsotopicFractionation(isotopes=isotopes_considered))
 
diff --git a/tests/unit_tests/physics/test_isotope_diffusivity_ratios.py b/tests/unit_tests/physics/test_isotope_diffusivity_ratios.py
index 2a501474c0..0216855d5e 100644
--- a/tests/unit_tests/physics/test_isotope_diffusivity_ratios.py
+++ b/tests/unit_tests/physics/test_isotope_diffusivity_ratios.py
@@ -161,10 +161,11 @@ def test_all_on_one_plot(plot=False):
         pyplot.xlabel("temperature [K]")
         pyplot.grid()
         pyplot.ylabel("diffusivity ratio (heavy to light)")
+        pyplot.legend()
         if plot:
             pyplot.show()
         else:
             pyplot.clf()
 
         # assert
-        assert 0.985 > max_value > min_value > 0.968
+        assert 0.985 > max_value > min_value > 0.948
diff --git a/tests/unit_tests/physics/test_isotope_relaxation_timescale.py b/tests/unit_tests/physics/test_isotope_relaxation_timescale.py
index 90de14024f..8fef206c78 100644
--- a/tests/unit_tests/physics/test_isotope_relaxation_timescale.py
+++ b/tests/unit_tests/physics/test_isotope_relaxation_timescale.py
@@ -85,15 +85,13 @@ def test_bolin_number_unit():
 
             # act
             value = sut(
-                const=Formulae().constants,
                 D_ratio_heavy_to_light=any_number_except_one * si.dimensionless,
-                D_light=any_number_except_one * si.m**2 / si.s,
                 alpha=any_number_except_one * si.dimensionless,
                 R_vap=any_number_except_one * si.dimensionless,
                 R_liq=any_number_except_one * si.dimensionless,
                 relative_humidity=any_number_except_one * si.dimensionless,
                 Fk=any_number_except_one * si.s / si.m**2,
-                rho_v=1 * si.g / si.m**3,
+                Fd=any_number_except_one * si.s / si.m**2,
             )
 
             # assert